R Notebook

Objective

Use your current knowledge in R, and research to learn more stuff and go beyond your current knowledge to create a comprehensive EDA for the Superstore dataset (here). Create visualizations and calculations to answer the following business questions.

library(dplyr) 
library(ggthemes)
library(ggplot2)
library(forcats)
library(lubridate)
library(scales)

superstore <- read.csv("Dataset/Sample - Superstore.csv") 

# Convert date colomns into Date format

superstore$Order.Date <- mdy(superstore$Order.Date)
superstore$Ship.Date <- mdy(superstore$Ship.Date)
superstore$Sales <- round(superstore$Sales, 2)
superstore$Profit <- round(superstore$Profit, 2)

# Delete the unknown '?' symbol from the product name.
superstore$Product.Name <- iconv(superstore$Product.Name, from = "UTF-8", to = "UTF-8", sub = " ")
superstore$Product.Name <- gsub("[^ -~]", " ", superstore$Product.Name)
superstore$Customer.Name <- iconv(superstore$Customer.Name, from = "UTF-8", to = "UTF-8", sub = " ")
superstore$Customer.Name <- gsub("[^ -~]", " ", superstore$Customer.Name)

Question 1: How does sales performance vary over time?

Objective: Identify monthly trends and seasonal patterns.

Tasks:

• Identify which months consistently show sales peaks

• Find any unusual dips in the trend line

• (optional) Calculate YOY growth rate for the most recent complete year

• Suggest potential reasons for seasonal patterns

```r
#Create separate columns with order year and month.
superstore$Order.Year <- year(superstore$Order.Date)
superstore$Order.Month <- month(superstore$Order.Date)
superstore <- superstore %>%
  select(Row.ID, Order.ID, Order.Date, Order.Year, Order.Month, everything())
```

```r
month_year_sales <- superstore %>%
  group_by(Order.Year, Order.Month) %>%
  summarise(
    Total.Sales = sum(Sales),
    Total.Profit = sum(Profit),
    .groups = "drop"
  )
month_year_sales
```

<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["Order.Year"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["Order.Month"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["Total.Sales"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["Total.Profit"],"name":[4],"type":["dbl"],"align":["right"]}],"data":[{"1":"2014","2":"1","3":"14236.90","4":"2450.18"},{"1":"2014","2":"2","3":"4519.92","4":"862.30"},{"1":"2014","2":"3","3":"55691.03","4":"498.71"},{"1":"2014","2":"4","3":"28295.35","4":"3488.84"},{"1":"2014","2":"5","3":"23648.27","4":"2738.72"},{"1":"2014","2":"6","3":"34595.12","4":"4976.55"},{"1":"2014","2":"7","3":"33946.37","4":"-841.50"},{"1":"2014","2":"8","3":"27909.47","4":"5318.08"},{"1":"2014","2":"9","3":"81777.32","4":"8328.08"},{"1":"2014","2":"10","3":"31453.37","4":"3448.20"},{"1":"2014","2":"11","3":"78628.73","4":"9292.16"},{"1":"2014","2":"12","3":"69545.63","4":"8983.56"},{"1":"2015","2":"1","3":"18174.08","4":"-3280.97"},{"1":"2015","2":"2","3":"11951.40","4":"2813.84"},{"1":"2015","2":"3","3":"38726.26","4":"9732.06"},{"1":"2015","2":"4","3":"34195.25","4":"4187.51"},{"1":"2015","2":"5","3":"30131.72","4":"4667.83"},{"1":"2015","2":"6","3":"24797.30","4":"3335.55"},{"1":"2015","2":"7","3":"28765.30","4":"3288.68"},{"1":"2015","2":"8","3":"36898.31","4":"5355.82"},{"1":"2015","2":"9","3":"64595.87","4":"8209.15"},{"1":"2015","2":"10","3":"31404.90","4":"2817.34"},{"1":"2015","2":"11","3":"75972.49","4":"12474.76"},{"1":"2015","2":"12","3":"74919.51","4":"8016.91"},{"1":"2016","2":"1","3":"18542.52","4":"2824.81"},{"1":"2016","2":"2","3":"22978.82","4":"5004.57"},{"1":"2016","2":"3","3":"51715.85","4":"3611.95"},{"1":"2016","2":"4","3":"38750.02","4":"2977.85"},{"1":"2016","2":"5","3":"56987.74","4":"8662.09"},{"1":"2016","2":"6","3":"40344.51","4":"4750.32"},{"1":"2016","2":"7","3":"39261.98","4":"4432.85"},{"1":"2016","2":"8","3":"31115.35","4":"2062.04"},{"1":"2016","2":"9","3":"73410.06","4":"9328.62"},{"1":"2016","2":"10","3":"59687.79","4":"16243.20"},{"1":"2016","2":"11","3":"79412.00","4":"4011.39"},{"1":"2016","2":"12","3":"96999.06","4":"17885.24"},{"1":"2017","2":"1","3":"43971.37","4":"7140.45"},{"1":"2017","2":"2","3":"20301.12","4":"1613.89"},{"1":"2017","2":"3","3":"58872.35","4":"14751.87"},{"1":"2017","2":"4","3":"36521.52","4":"933.26"},{"1":"2017","2":"5","3":"44261.08","4":"6342.68"},{"1":"2017","2":"6","3":"52981.70","4":"8223.39"},{"1":"2017","2":"7","3":"45264.42","4":"6952.58"},{"1":"2017","2":"8","3":"63120.85","4":"9041.06"},{"1":"2017","2":"9","3":"87866.64","4":"10991.54"},{"1":"2017","2":"10","3":"77776.91","4":"9275.30"},{"1":"2017","2":"11","3":"118447.79","4":"9690.10"},{"1":"2017","2":"12","3":"83829.31","4":"8483.34"}],"options":{"columns":{"min":{},"max":[10],"total":[4]},"rows":{"min":[10],"max":[10],"total":[48]},"pages":{}}}
  </script>
</div>

```r

year_sales <- superstore %>%
  group_by(Order.Year) %>%
  summarise(
    Total.Sales = sum(Sales),
    Total.Profit = sum(Profit),
    .groups = "drop"
  )
year_sales
```

<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["Order.Year"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["Total.Sales"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["Total.Profit"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"2014","2":"484247.5","3":"49543.88"},{"1":"2015","2":"470532.4","3":"61618.48"},{"1":"2016","2":"609205.7","3":"81794.93"},{"1":"2017","2":"733215.1","3":"93439.46"}],"options":{"columns":{"min":{},"max":[10],"total":[3]},"rows":{"min":[10],"max":[10],"total":[4]},"pages":{}}}
  </script>
</div>

```r


# month to month plot for Sales
ggplot(month_year_sales, aes(x = factor(Order.Month), y = Total.Sales, fill = factor(Order.Year))) +
  geom_bar(stat = "identity", position = "dodge") +
  facet_wrap(~Order.Year, nrow = 1) +
  labs(title = "Total Sales by Month and Year",
       x = "Month",
       y = "Total Sales") +
  theme_minimal() +
  theme(legend.position = "none", 
        axis.title.y = element_blank(),
        plot.title = element_text(hjust = 0.5)) +
  scale_y_continuous(labels = comma) 
```

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" />

```r


# month to month plot for Sales and profit
ggplot(month_year_sales, aes(x = factor(Order.Month))) +
  geom_bar(aes(y = Total.Sales, fill = "Total Sales"), stat = "identity", position = position_dodge(width = 0.9), width = 0.8) +
  geom_bar(aes(y = Total.Profit, fill = "Total Profit"), stat = "identity", position = position_dodge(width = 0.9), width = 0.8) +
  facet_wrap(~Order.Year, nrow = 1) +
  labs(title = "Total Sales and Profit by Month and Year",
       x = "Month",
       y = "") +
  theme_minimal() +
  theme(legend.title = element_blank(), 
        legend.position = "top",  
        axis.title.y = element_blank(), 
        plot.title = element_text(hjust = 0.5)) +
  scale_y_continuous(labels = comma)
```

<img 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" />

```r
  
# YOY plot for Sales and Profit
ggplot(year_sales,aes(x = factor(Order.Year))) +
  geom_bar(aes(y = Total.Sales, fill = "Total Sales"), stat = "identity") +
  geom_bar(aes(y = Total.Profit, fill = "Total Profit"), stat = "identity") +
  labs(title = "Total Sales and Profit by Year",
       x = "Year",
       y = "") +
  theme_minimal() +
  theme(  plot.title = element_text(hjust = 0.5)) +
  scale_y_continuous(labels = comma) +
  guides(fill = guide_legend(title = NULL)) 
```

<img 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8S1suDWyPnnn98p+7T29cPq4IMPLuscn58sADiSzYFZ7isMitdDDz10JAvWT1rfLGg8svvuu3e8ThYcG8kehR6Jz0Rx3XjNgm6TXrfTCeedd96o62SBqk6njbvvjW98Y5l/k002GXPeM5/5zPJ4FjgdyYJr5fui/lkAeCQLkJd5e/2+yOZTHVNGUVbx+tznPrcs74UvfGF5/kknnZTvn+53TnnRcTayuVLLsv7jP/5jJFs4aST7xU25r6hfvGbzHI/8z//8T8crZb/4GZUnrjNZygLQI/H5iWvHPdvNZ7HTNbPH7Ef23nvvsvy45hVXXDHm1Cw4Wp6zzTbbjGQLbY05p9OOfnynFNftx30e15rO57iog1cCBAgQIECg3gKp3tVTOwIECBCos0AvAdNsBNJINmKr/I9zERDIVlceyUYMjtqfjaYa+dCHPjSGYjrBi+OPP37UtaPcCFQuXrw4fy3qUbxmI61GslFIY8qeSvBwTOaWHXfddddINoqurNMWW2wxEsGqfqV+tTfq002bsxF5I8961rPK9hSO0cZspO+o/dHX2VysHZuajdIcCfsif7xmi1qNZIsHjblOBGa+9KUvdbzOVHb2y6o1YJqNBh3JRieOakdrm4rtOKc1ONhe7wgKZ488j7pO3CftQekjjjhi1DmDCpgeeOCBZT2e/exntzdnVKDpv//7v8tzC494fepTn1rm68f3xbAETKOea6yxRmkS902nvn/HO94xEkHK9hRuheP/+T//p/3wmPcf/vCHy/Nf+tKXjjk+lR3Z6NGRuK+L8iPA31rH1r7ORlmPZE8WTHr5fn2nFAX16z6P67UGTFvbVrS//XNc1MErAQIECBAgUH8BAdP695EaEiBAoLYC0w2YxojIGGFY/KeyCIhGUCT+c33nnXeOXHDBBSPZo9vlOXFutjr8KIsINmaP049kj+CX58W1Yl/xkz2iX+a57rrrRgVFX/SiF41kjwGXIzSXL1+ej/DM5mAcdd6b3vSm8hrFRjfBw+LcyV6PO+64sv6FSfa4dj7qLnuEu6zfZNdpP97P9sa1u2nzQQcdVLYlApzvfve7R7KVs/OqRfAu2nPAAQeU50SwutMotOyx+/KcGIGZPR5dBl7i83PRRReNbLvttuU5MWK4l9RPq9aAaYySjD6NgGi2MNdIfMazx6ZHPv/5z49yiHMicNUpxSjV1oDZYx/72PxeiPskPt/xGX7lK19ZWhSfoXgdRMA0e3x8VH07Be1aA01rr712XvcItMWozgi2xn0cIy0j9ev7IhyL74XWoN6FF15Y7s+mtii7oNMI06l+55QXm2SjdYRp0X/xmYnvwaVLl+YGP/3pT0cF6OK8CNK1p0996lPlZyFbTKm8b9rPK97vuOOO5fnf/va3i93Tfv3EJz5RXi/qGPWJFL8QWGeddcpj73nPe7oqo1/fKVFYP+/zuN5UPsdxvkSAAAECBAgMj4CA6fD0lZoSIECgdgLTDZi+733vK//THP+BjkBApxTBnghqFgGEeLQ3ggftKUaAFuesttpq7YfL9//yL/9SnnfIIYeU+zttvOUtbynPjaBXe+omeNieZ7z3ESTOVuIuyyvaUrxG+TE9wIknnjhy5ZVX5kGy8a7Vur+f7Y3rTtbm1se4I+A13uixCPIdddRRZXuzOSxbqz0So9SKtmfzKY7b3mzuyfK8OH+8z9Goi4/zpp9WrQHTqNf+++8/smzZsjElZ4vijETbi7bG6OJOKUb9FefEI8wRcO2UYlqM4rziteqAaTZP68huu+1W1iNGR2bzPI6pbmugKeoaeVofzY7H74u69/v7IirTOnp5vKkhOgVMi4Z0+51TnD/Za3vANEaJdhpxHPfOP/7jP5a+EVRdsWLFqMvHI/WtI48vvvjiUcdb32TzC5fXiu/X1tGgredNZTuusc8++5TXzeaFHYlpF17zmteU+7K5WLv6RVC/vlOK+vfzPo9rTuVzXNTBKwECBAgQIDAcAgKmw9FPakmAAIFaCkwnYBqjvGLkYRHQOeWUUyZsW4yiizn7ivPf/va3jzm/m+BFBGBaH30fb17U4uIxorEoM17b5/WbLHhYXGcqr5/+9KdHIrjQWm6n7Q022CAPOE40P2G/2xvtmKjNEbTJFi8q6/7BD35wwqbH+RHsKdoXIy6L9IUvfKHcH4GkiVIEj2JOxHjNFkKa6NRxj/XbqjVgGp/1GFU6Xop5SQuDeI3Pe2uKoFkxv2QcP/vss1sPj9lun5u3CDqOOXGSHa2Bqig3RvRGgLr9J6Yc+PKXvzzysY99bOQVr3jFSDxm3dqeN7/5zR1Lag00RXD9mmuu6XjeTHxfREF1DpjGVAsRyBwvxWekGLkc1jE1SXt62cteVvZDtmJ9++Hy/dFHH12e1+m7tTxxihu/+93vRj2aHwHUYqqVCOZOdE8URfXzOyWu2e/7PK7Z7ec4zpUIECBAgACB4RIQMB2u/lJbAgQI1EpgOgHTWKykCKjEiKZOI+/aG9k6ci4Wz2lP3QRMI08sXBOPL3czR2iMdivqGa8xj15rmih42HreVLdj1OHnPve5fERp6zyGrXUptiOQlq0wPRKPCXdK/WxvXH+iNsdo0qJeMaqwdbRgp7rFvtaRg61zJ7YuXBPBtK985SvjXaJv+/tp1RowjdGlE6WYBqJwi9di+oIiz1lnnVUej2kKWqeYKM5pfW09P67Xr4Bpax273Y7RwRGk6pRaA0277LJLp1PyfTPxfREXrnPAdLLR71H/WCyv6IeY87Q9xajS4ni0Nb5X2lNMdbD++uuX58Xj6v1MsUhTUYfW13hkv5vUz++Uorx+3udxzW4/x0X5XgkQIECAAIHhEVgp+weMRIAAAQIEKhPIRpKVZWWPnaZsBF75fryNbHRSeSgLppXbU93I5i1M2Xx9KZsjcdysWUAqZaP+UrYq9qhzsuDCqPcz9SYLEKZsGoKUreKcssdY0yWXXJKOPfbYtOeee46xirqefPLJ6RnPeEbKAlNjqlRle7NgS1l+NvdsyqZGKN+Pt5EFyspDrfmzwGDKgvH5sSygnv72b/82Pf3pT0/Zwl8pG1Vb5unnxkxZFe0Yr67ZiMxRVlkAddSp2ajZ8v2uu+6asjlfy/edNrLRtp12V7ovC/SnN7zhDSkbfZqyuVcnLTubimDccwb5fTFupWb4QHzWJ0vZYkrlKdlozXK72IhrPO5xj8vfZr9QSeeee25xqHz91re+lW666ab8fTY1RJqoH8pMU9jIHsFP7Z/H+O7NFibr6iqt3wm9fqcUBc7UfR7X77dfUWevBAgQIECAwGAEVh5MsUolQIAAgdkq0BoAmSyYVBgV//GP99mj8fl/8rORUcXhab1GIC4bbZqyR/NTtkhNiqBDBGNjOxuNNeaa2e9Cx+yb6R3ZSM30tKc9Lf9517velbLRgimCHNkj6ykbSZiKIO5PfvKTlD12m7LH2set0ky3tzW4kU1nkLLRw+PWpTiQPXJbbKbW/LEzm54gZSM1UwR7IkXgOH6yR7zThhtumLKRXelZz3pW/jp//vz8nH790U+rbhzilwbRt5Gy+R9HNSObr7R8ny32VG6Pt5FN6ZDCo9NneLw83eyPX25kI5rHnBoB3AhCZaMY02abbZYiqLv33nunCJp2m7KpGcY9tS7fF+NWcAYOdPO9mC3mVJYc31nx/ZQ98l7ui+2Xv/zlKb43ImWLQ6VsJGq+Xfxx+umnF5spm06h3O7XRtThve99b8oWkiovuWTJknJ7so3W74R+fKe0l9fP+zyuPdHnuL1s7wkQIECAAIH6CwiY1r+P1JAAAQKNEshWqS7b001gIE7O5jDNgzLFKMpskZY03YDpX/7yl5TNm5qyx0LTbbfdVtalfSNGxmULTLXvHuj7GLWZzemZ/2Srz+cjL7MFdfI6ffGLX8yDI9miQKPqWFV7W4Mb2eP46cYbbxxVj8ne3H777SmbmzFlczPmp8ao4hih+IIXvCC1fmbiYDavZcpW3s5/snlp0+GHH57e//73jxmBO1mZ7cdnwmrRokXtxUzpfTYVRHl+NwHTODnOiyBaP9P555+f34P9vGZxrYmCyq19P4jvi6KOVb5O5FHUozVgGsH2W265Ja233nrF4fw1AqbxPRHB1G984xv5iPX4Lo2UPZqej2KP7WxO0ZRNAxCbfU/tI82jrG5Tv79TinJn4j6Pa3fTb0UdvBIgQIAAAQL1FxAwrX8fqSEBAgQaJRCPnBep21Fw8eh567lT+U93UVa8ZnOdpr/5m7/JA26t+2M7gg3xuH62aE6KYN1TnvKUfNRccV7r6K1iX6+vEZDNFkfJgx0R1Nhvv/26vmSM5ovH9uM1UuTP5mZNrQHTKtvbOjIy6rDddtt13ZbixGhDa9p2221TjDD8/ve/n4+q/eY3v5mPBG49J0Ycf/SjH00/+tGP0pe+9KV89Gnr8W63q7Tqtk5xXjyyX6QYEddNar3Hujl/0Od0Grla1Km1La3fAcXxTq/9+r7odO0q9nXTz8WI5KhP+C1evHhM1SLAHKN9swW7Ukz1EL9UicfkI8W9Er/YiBRTXsQvHuqWZuI7ZSbv84k+x3WzVR8CBAgQIEBgcgEB08mNnEGAAAECfRSIxxbjP/CRsgVu8tfJ/ogRhcXj53HuOuusM1mWMcdjdGrMnxfXihSBqGzBpPyR7giUrr322qPyxGjH1tQezGs9Nt3tGLUXIygjxWPUMZpwKo8yx1QF8Qh0tjhKfo0YeVukqtsb844WKVvsJ51xxhnF255fY37F+IkUAeYInEZw+MILLywfZb/00kvTW97ylmmVW7XVVEBaR1LfcMMNXWWd6ujeri46oJMG9X0xoObmxU408r2oV+tnYeONNx4VWC/OiddXvvKV5fdttphcGTCNR/SLNBOP4xfX7uW1398pdb7Pe3GSlwABAgQIEJgZAQHTmXF1VQIECBAYR6B1nrduA6at58Wcie3BzXGKGrU7grQxD16kmBs05gKNhZTGS/GIeGtqDdi27u9lu3U0aIye+/rXv55e/OIXT+mSrQG11sdfq25va3DjyiuvnFIbpnJyBIn/6Z/+Kf+JRbFiO0bORYoAagS2pzoauGqrqbS39THf1iDZeNe444478setxzs+bPsH9X0xSKfilzoT1aH1szDRYkMxb+nrXve6fO7nmOIiHkePXxZ997vfzS8fj/a3Lqo3UZlVH+v3d0qd7/OqbZVHgAABAgQITC4w8VKrk+d3BgECBAgQmJJAawAkAlzdjKZqHQ0Vj8q3PqbbbeHf+c53ylNjtOJEwdI4MR7xbk3xmG+/U1i0jpad6qjMmIcwDIu01VZbFZup6va2Bjdi8azW0a5lpdo2zjzzzPSkJz0pnz+xWJwmTjnxxBPzffFYfwQ5xksxH+Opp55arhwfwaBYxGuqqWqrqdQvFrYq0sUXXzzp/dL6eSjyDfProL4vBmn2ta99bdLi494pUut9X+wrXuOXKIceemj+Nr7D4pcyMTq7+AXQS1/60vL+KfLU5bWf3ynRpjrf53UxVw8CBAgQIEDg/wsImP5/C1sECBAgUIHAM57xjBSPkEaKgN9kqyZH4O0zn/lMWbNOi5O0jqwcb/6/P//5z+U11l133XK700YsoHLccceNOlQEGEbt7PFNjPQ66qijyqvEwizHHnts+X6ijVhh/m1ve1s5t2vM6xpzERap6vbutNNOKR7FjxRzD/7zP/9zUZWOr+EZQdKYTuDss8/OpyMoToyFfmJfBD+/8IUvFLs7vkZZrdMlFItGdTx5nJ1VW41TjY674xcEG2ywQX4sHin+4Ac/2PG82BkWschPk9JMfF/0w6eb75zplhMBzdYFj9qvE/dMBD4jxWj5ye61eCy/SDHv8Ve/+tXibYqFoeqa+vmdEm2s831e1z5QLwIECBAgMJsFBExnc+9rOwECBAYgEIGGj3zkI2XJJ598cvr3f//38n3rxm9/+9t8js8iWLnhhhumGBHVnloXgYqg0U9/+tP2U/IFnYqd8Tj+TTfdVLwd9RqPeUeQ5tprrx21P4K7M5Fe+9rXjlpd+T3veU8+X2cETTqNvo3g4M9//vN8UapTTjmlrNIRRxyRHvOYx5TvY17WIlXR3pgqIepTPA4f87Mec8wx5Ui2oi7xGm2IeRML48hz5JFHlqe88IUvLLdjBGlYjJfCqwiYxhQH8RmZaqraair1C5v3vve9ZZaTTjopxVyU7SnukQi+F9NOtB8f1vcz8X3RD4tuvnOmW04s0LTvvvt2nOM5AqmHHXZYeem47ycaYRonxkJ3xTkxArm4nyIY3zqKs7xoTTb6+Z0STarzfV4TctUgQIAAAQIEWgQETFswbBIgQIBANQIxr14EBCJFcCDmoXz+85+fTjvttBTzXxYjLXfeeef0s5/9LD8v/vMcj+YvWrQof9/6RwRVYpX7IsW1DzrooHxBp2JU0fOe97zyUf6YnzRWj46VomO+wHiU+ytf+UoecIpFlK6++uo88Ldw4cLikvk55Zs+bqy11lopgovxeHmRYq7BeBQ75mqN0bhhE6PEdt999xTnx3/8i4WeIs9+++2X3ve+9xXZ89dBtDcCMC972cvKesTo4ajzJz7xifTDH/4wXXHFFek///M/85GordMPHH/88Wnrrbcu80XfxAJdkSIAHn0ZI+FiZFwEi6PtMfI0Atsf/ehH8/NipF2UVwRs851d/jEIqy6rlp8WfR99HClGUL/kJS/JPw/x+Y37Je6bmIcyfvkQ90nr6Mc805D/0e/vi35wdPOdM91yFixYkGI196c97Wn5Lx1iWop4nDwC57vttluKKS8iLV68uOsR6cUo05gr+e67787z13Wxp7xyj/zRr++UuFzd7/PWdtsmQIAAAQIEaiCQjcqQCBAgQIDAtAQ23XTTkeyvsvznAx/4wJSukf2nfeTVr351mb+4TqfXLHA4kq2MPuH1s8fTO14rG01V5stG53U8p73MbKTmyLnnnjuSjdgrzz/66KPL68TGXnvtVR7L5hMcdWw6b/73f/935KlPfWp5zfY6dXqfBQdHskDISFh2Sv1sb1y/mzZnAb2RbGTpyNy5c7tqy5vf/OZOVR+58847R7IRo11dI/orm9+z43W63dlPq4MPPrisdxYsnrQKWRC8PD+bjqDj+dnj+CNZEL08r9PnIZviYeTzn//8KLf777+/4/Um2xn3TWsZ2QjrybJM6fgzn/nM8vpZwHfSvP3+vmg1z6b96Fh+NtK5rGN8PtpTN9857XnGe/+c5zynLCt73H4km7u1fN/aD8V2Nv3FSDbadLzLjdmf/WJo1D2ZBXzze2zMiX3ekf3Ca1Q7slHlUy6hX98pUXA/7/O43lQ/x5FHIkCAAAECBIZDwAjT7F+eEgECBAhUL7DGGmvkC/bEaNLtt98+ZQG2MZWIR8zf9KY35aNM999//zHHW3fE6Ks3vvGN5XyPxbGYD7NI8dh3lPeEJzyh2DXqNVaMjnlBYwTXs5/97NRaZiyyEqMdZyptu+226Xvf+17+uPVzn/vcFKPMxksx8jUeW4+VrmN0YVh2SoNo77x589IJJ5yQL5oVjwKPt0BXjCCNhbWyQHunqucjaWNEaowgHe8x+xhVHKPGLrvsspQFcztep9udg7Dqtm5xXnweYoRtjCLdZJNNxmSNxZFilHTrdAZjThriHf3+vugHRTffOdMpJ/tFVPr+97+ff//E/dSaYoT961//+vze2WKLLVoPTbgd8+AecMAB5Tkxaj1Gqw9D6td3SrS17vf5MPSHOhIgQIAAgdkiMCfiurOlsdpJgAABAvUViEdFr7nmmnyhnwgORVAoVklvDxh004KYnzR+IgAaj7W3pwh8/v73v89Xco9H9jfffPO8rOksGNR+7X69j0evs1Gn+VQAMWXAfffdl2KxqvXXXz9/JH/+/PldFzXI9sbK3DHvYrQl5tkM68022yx/nLjbBkS+eET5xhtvzH9i+oKYriEs+p0GadVtW8IjPMM17o94bLnT57zb6w3jef38vuhH+yf7zpluGdHO+IVATB2yyy67pAiMTzfF1AZf/vKX8+wXXHBBPqXFdK81yHz9+E4Zhvt8kMbKJkCAAAECBFISMPUpIECAAAECBAgQINBggZi3OUaZxpzRMYI1FtSbzny/DSbSNAIECBAgQIDAKAGP5I/i8IYAAQIECBAgQIBAswRisbUIlkY6/PDDBUub1b1aQ4AAAQIECMyAgBGmM4DqkgQIECBAgAABAgTqIPDjH/84ZYsTpWwhtRRTedxwww1pnXXWqUPV1IEAAQIECBAgUFuBlWtbMxUjQIAAAQIECBAgQGBKArfccks6+OCD80fvr7/++nTllVemFStW5Nc44ogjBEunpOlkAgQIECBAYLYKGGE6W3teuwkQIECAAAECBBonEIsirbrqqileW1MsGBXB01hUTyJAgAABAgQIEJhYwBymE/s4SoAAAQIECBAgQGBoBObOnZs23HDDsr4rr7xy2m+//dLFF18sWFqq2CBAgAABAgQITCxghOnEPo4SIECAAAECBAgQGCqBW2+9NR9N+sADD6S99947rbnmmkNVf5UlQIAAAQIECAxaQMB00D2gfAIECBAgQIAAAQIECBAgQIAAAQIEaiPgkfzadIWKECBAgAABAgQIECBAgAABAgQIECAwaAEB00H3gPIJECBAgAABAgQIECBAgAABAgQIEKiNgIBpbbpCRQgQIECAAAECBAgQIECAAAECBAgQGLSAgOmge0D5BAgQIECAAAECBAgQIECAAAECBAjURkDAtDZdoSIECBAgQIAAAQIECBAgQIAAAQIECAxaQMB00D2gfAIECBAgQIAAAQIECBAgQIAAAQIEaiMgYFqbrlARAgQIECBAgAABAgQIECBAgAABAgQGLSBgOugeUD4BAgQIECBAgAABAgQIECBAgAABArUREDCtTVeoCAECBAgQIECAAAECBAgQIECAAAECgxYQMB10DyifAAECBAgQIECAAAECBAgQIECAAIHaCAiY1qYrVIQAAQIECBAgQIAAAQIECBAgQIAAgUELCJgOugeUT4AAAQIECBAgQIAAAQIECBAgQIBAbQRWrk1NVIQAgcYKjIyMpPiJNGfOnPynsY3VMAI1F3A/1ryDVG9WCbgfZ1V3a2zNBdyPNe8g1SNAgEDFAgKmFYMrjsBsFLjnnnvS0qVL86YvWrQozZ8/fzYyaDOBWgjce++9KX4irbXWWmn11VevRb1UgsBsFLjvvvvS3XffnTd9zTXXTAsWLJiNDNpMoBYC999/f7rrrrvyuqyxxhpp4cKFtaiXShAgQIDAYAQ8kj8Yd6USIECAAAECBAgQIECAAAECBAgQIFBDAQHTGnaKKhEgQIAAAQIECBAgQIAAAQIECBAgMBgBAdPBuCuVAAECBAgQIECAAAECBAgQIECAAIEaCgiY1rBTVIkAAQIECBAgQIAAAQIECBAgQIAAgcEICJgOxl2pBAgQIECAAAECBAgQIECAAAECBAjUUEDAtIadokoECBAgQIAAAQIECBAgQIAAAQIECAxGQMB0MO5KJUCAAAECBAgQIECAAAECBAgQIECghgICpjXsFFUiQIAAAQIECBAgQIAAAQIECBAgQGAwAgKmg3FXKgECBAgQIECAAAECBAgQIECAAAECNRQQMK1hp6gSAQIECBAgQIAAAQIECBAgQIAAAQKDERAwHYy7UgkQIECAAAECBAgQIECAAAECBAgQqKGAgGkNO0WVCBAgQIAAAQIECBAgQIAAAQIECBAYjICA6WDclUqAAAECBAgQIECAAAECBAgQIECAQA0FBExr2CmqRIAAAQIECBAgQIAAAQIECBAgQIDAYAQETAfjrlQCBAgQIECAAAECBAgQIECAAAECBGooIGBaw05RJQIECBAgQIAAAQIECBAgQIAAAQIEBiMgYDoYd6USIECAAAECBAgQIECAAAECBAgQIFBDAQHTGnaKKhEgQIAAAQIECBAgQIAAAQIECBAgMBgBAdPBuCuVAAECBAgQIECAAAECBAgQIECAAIEaCgiY1rBTVIkAAQIECBAgQIAAAQIECBAgQIAAgcEICJgOxl2pBAgQIECAAAECBAgQIECAAAECBAjUUEDAtIadokoECBAgQIAAAQIECBAgQIAAAQIECAxGQMB0MO5KJUCAAAECBAgQIECAAAECBAgQIECghgICpjXsFFUiQIAAAQIECBAgQIAAAQIECBAgQGAwAgKmg3FXKgECBAgQIECAAAECBAgQIECAAAECNRQQMK1hp6gSAQIECBAgQIAAAQIECBAgQIAAAQKDERAwHYy7UgkQIECAAAECBAgQIECAAAECBAgQqKGAgGkNO0WVCBAgQIAAAQIECBAgQIAAAQIECBAYjICA6WDclUqAAAECBAgQIECAAAECBAgQIECAQA0FVq5hnVSJAAECBAgQIECAAAECjRW4Y/nyxrZtWBt2/4oV6e4HH8yr/1C2vUIf1a4rF82bV7s6qRABAs0VEDBtbt9qGQECBAgQIECAAAECNRTY5oKLalgrVSJQb4GbDzqw3hVUOwIEGiXgkfxGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi4CAaS968hIgQIAAAQIECBAgQIAAAQIECBAg0CgBAdNGdafGECBAgAABAgQIECBAgAABAgQIECDQi8CckSz1cgF5CRQCt956a3rooYeKt14JECBAgAABAgQIEOgg8MQrruqw1y4CBCYSuHKXHSc6XMmxefPmpcWLF1dSlkIIEBiswMqDLV7pTRJYuHBhevjhh5vUJG3pk8CyZcvS8uXL86vNnz8/rbLKKn26sssQIDBVAffjVMWcT2DmBOLvxrgnI6266qop/iMuESBAgEBngTXWWKPzgQr3zp07t8LSFEWAwCAFBEwHqd+wsldfffWGtUhz+iUQgfQiYLraaqulCJpKBAgMRiAeLCnuxwjQ+O4eTD8olUAILF26dFTAdMGCBWAIECBAYByBGKAjESBAoCoBc5hWJa0cAgQIECBAgAABAgQIECBAgAABAgRqLyBgWvsuUkECBAgQIECAAAECBAgQIECAAAECBKoSEDCtSlo5BAgQIECAAAECBAgQIECAAAECBAjUXkDAtPZdpIIECBAgQIAAAQIECBAgQIAAAQIECFQlIGBalbRyCBAgQIAAAQIECBAgQIAAAQIECBCovYCAae27SAUJECBAgAABAgQIECBAgAABAgQIEKhKQMC0KmnlECBAgAABAgQIECBAgAABAgQIECBQewEB09p3kQoSIECAAAECBAgQIECAAAECBAgQIFCVgIBpVdLKIUCAAAECBAgQIECAAAECBAgQIECg9gICprXvIhUkQIAAAQIECBAgQIAAAQIECBAgQKAqAQHTqqSVQ4AAAQIECBAgQIAAAQIECBAgQIBA7QUETGvfRSpIgAABAgQIECBAgAABAgQIECBAgEBVAgKmVUkrhwABAgQIECBAgAABAgQIECBAgACB2gsImNa+i1SQAAECBAgQIECAAAECBAgQIECAAIGqBARMq5JWDgECBAgQIECAAAECBAgQIECAAAECtRcQMK19F6kgAQIECBAgQIAAAQIECBAgQIAAAQJVCQiYViWtHAIECBAgQIAAAQIECBAgQIAAAQIEai8gYFr7LlJBAgQIECBAgAABAgQIECBAgAABAgSqEhAwrUpaOQQIECBAgAABAgQIECBAgAABAgQI1F5AwLT2XaSCBAgQIECAAAECBAgQIECAAAECBAhUJSBgWpW0cggQIECAAAECBAgQIECAAAECBAgQqL2AgGntu0gFCRAgQIAAAQIECBAgQIAAAQIECBCoSkDAtCpp5RAgQIAAAQIECBAgQIAAAQIECBAgUHsBAdPad5EKEiBAgAABAgQIECBAgAABAgQIECBQlYCAaVXSyiFAgAABAgQIECBAgAABAgQIECBAoPYCAqa17yIVJECAAAECBAgQIECAAAECBAgQIECgKgEB06qklUOAAAECBAgQIECAAAECBAgQIECAQO0FBExr30UqSIAAAQIECBAgQIAAAQIECBAgQIBAVQICplVJK4cAAQIECBAgQIAAAQIECBAgQIAAgdoLCJjWvotUkAABAgQIECBAgAABAgQIECBAgACBqgQETKuSVg4BAgQIECBAgAABAgQIECBAgAABArUXEDCtfRepIAECBAgQIECAAAECBAgQIECAAAECVQkImFYlrRwCBAgQIECAAAECBAgQIECAAAECBGovIGBa+y5SQQIECBAgQIAAAQIECBAgQIAAAQIEqhIQMK1KWjkECBAgQIAAAQIECBAgQIAAAQIECNReQMC09l2kggQIECBAgAABAgQIECBAgAABAgQIVCUgYFqVtHIIECBAgAABAgQIECBAgAABAgQIEKi9gIBp7btIBQkQIECAAAECBAgQIECAAAECBAgQqEpAwLQqaeUQIECAAAECBAgQIECAAAECBAgQIFB7AQHT2neRChIgQIAAAQIECBAgQIAAAQIECBAgUJWAgGlV0sohQIAAAQIECBAgQIAAAQIECBAgQKD2AgKmte8iFSRAgAABAgQIECBAgAABAgQIECBAoCoBAdOqpJVDgAABAgQIECBAgAABAgQIECBAgEDtBQRMa99FKkiAAAECBAgQIECAAAECBAgQIECAQFUCAqZVSSuHAAECBAgQIECAAAECBAgQIECAAIHaCwiY1r6LVJAAAQIECBAgQIAAAQIECBAgQIAAgaoEBEyrklYOAQIECBAgQIAAAQIECBAgQIAAAQK1FxAwrX0XqSABAgQIECBAgAABAgQIECBAgAABAlUJCJhWJa0cAgQIECBAgAABAgQIECBAgAABAgRqLyBgWvsuUkECBAgQIECAAAECBAgQIECAAAECBKoSEDCtSlo5BAgQIECAAAECBAgQIECAAAECBAjUXkDAtPZdpIIECBAgQIAAAQIECBAgQIAAAQIECFQlIGBalbRyCBAgQIAAAQIECBAgQIAAAQIECBCovYCAae27SAUJECBAgAABAgQIECBAgAABAgQIEKhKQMC0KmnlECBAgAABAgQIECBAgAABAgQIECBQewEB09p3kQoSIECAAAECBAgQIECAAAECBAgQIFCVgIBpVdLKIUCAAAECBAgQIECAAAECBAgQIECg9gICprXvIhUkQIAAAQIECBAgQIAAAQIECBAgQKAqAQHTqqSVQ4AAAQIECBAgQIAAAQIECBAgQIBA7QUETGvfRSpIgAABAgQIECBAgAABAgQIECBAgEBVAgKmVUkrhwABAgQIECBAgAABAgQIECBAgACB2gsImNa+i1SQAAECBAgQIECAAAECBAgQIECAAIGqBARMq5JWDgECBAgQIECAAAECBAgQIECAAAECtRcQMK19F6kgAQIECBAgQIAAAQIECBAgQIAAAQJVCQiYViWtHAIECBAgQIAAAQIECBAgQIAAAQIEai8gYFr7LlJBAgQIECBAgAABAgQIECBAgAABAgSqEhAwrUpaOQQIECBAgAABAgQIECBAgAABAgQI1F5AwLT2XaSCBAgQIECAAAECBAgQIECAAAECBAhUJSBgWpW0cggQIECAAAECBAgQIECAAAECBAgQqL2AgGntu0gFCRAgQIAAAQIECBAgQIAAAQIECBCoSkDAtCpp5RAgQIAAAQIECBAgQIAAAQIECBAgUHsBAdPad5EKEiBAgAABAgQIECBAgAABAgQIECBQlYCAaVXSyiFAgAABAgQIECBAgAABAgQIECBAoPYCAqa17yIVJECAAAECBAgQIECAAAECBAgQIECgKgEB06qklUOAAAECBAgQIECAAAECBAgQIECAQO0FVq59DadRwR/84Afp0ksvTb/+9a/TnDlz0mabbZb+/u//Pm288cYdr7Zs2bJ09tlnp8svvzzdcccdacstt0w77bRTOvDAA9PcuXP7lqfjhR7Z+atf/Sp98YtfTDfccENasGBB2n777dM+++yT1328fFXlGa98+wkQIECAAIHhEbhj+fLhqewsqel9K1akex58MG/tw9n2cn1Uu55fNG9e7eqkQgQIECBAgMDMC8wZydLMF1NNCSuyf2iefPLJ6Stf+Upe4FprrZUeeOCBFAHRCHy+/e1vT/vtt9+oytx5553pta99bfrDH/6Q73/0ox+d/vrXv+bbe+65Zzr22GPTvLZ/KE0nz6hC295EsPakk07K9y5cuDD/x3L8g3m11VZLS5YsSTvvvHNbjpQHeKvIM6ZgOwhMQ+Duu+9OS5cuzXMuWrQozZ8/fxpXkYUAgX4I3HPPPenee+/NLxV/T66++ur9uKxrDIHAeueePwS1VEUC9RK4+aADZ6RC7scZYXXRhgvM1P3YcDbNI0BgmgKNeiT/tNNOy4Ola6+9djrllFPS1772tfTNb34zvfrVr04PPfRQev/7359uueWWUVTHHXdcHix98pOfnM4999z01a9+NZ111llp8803T5dcckn62Mc+Nur8eDOdPGMu8siOq6++Oi8jgrLHH398+sY3vpHOP//8dOSRR6b7778/HX300enmm28elb2qPKMK9YYAAQIECBAgQIAAAQIECBAgQIDALBBoTMA0Rq/FyNKVVlopvetd70o77rhjvh0jS1/+8pfnj9nHaNPvfve7Zbf+8pe/TD/5yU/ykZzvfe97U4y0ibThhhumj3zkI/mo1PPOOy/FaJwiTSdPkbfT62c/+9kUg3wPO+ywFCNaYwqBVVZZJR1yyCHpBS94QYpRs+ecc86orFXlGVWoNwQIECBAgAABAgQIECBAgAABAgRmgUBjAqYRVIygaQQZd9hhhzFd98Y3vjG96U1vSttuu2157Dvf+U6+/fSnP33MI8LxaP5uu+2WPx4fQdMiTSdPkbf99b777ssDtrH/gAMOaD9c7ouRrw8+Mr9VVXnGVMYOAgQIECBAgAABAgQIECBAgAABArNAoDEB01iwKdIee+zRsdu222679PznP39UwPQXv/hFfm48jt8pRcA00s9//vPy8HTylJnbNq655pp8dOlGG22UNthgg7ajKW299dZpjTXWSHfddVe68cYb8+NV5RlTGTsIECBAgAABAgQIECBAgAABAgQIzAKBlZvSxttuuy1vyhZbbJGvNB+jQq+66qr8kfatttoqHXrooWnjjTce1dw//elP+ftHPepRo/YXb4r9xYJQsX86eYrrtb9Odq04P+oQUwJEHTbbbLNJy+9Xnva6ek+AAAECBAgQIECAAAECBAgQIEBgNgg0JmB666235nOW/u53v0tvfetb8yBjzAUac4D++te/ThdccEG+f9999y37tVi1uwiMlgce2VhzzTXzreK8eFNsTyXPI5cb8zLZtSJDex2qyjOmsl3suP322/PFtbo41SmzTODhhx8uWxwjpu++++7yvQ0CBKoVaL0f41689957q62A0ggQIDBEAvF/DIkAgXoI1OF+jMWax4sF1ENJLQgQ6JdAIwKmMa9n/MQCTzFXaTx+f9RRR6VNNtkkRRDv1FNPzVefX7JkSX5s3XXXTfEfxlgEKlI89t4pLVy4MN+9bNmy/HU6eTpdt9gXdY40XvlxrKhDUdeq8kTZU00PPfSQgOlU0Wbh+a3BmlnYfE0mUCuBWHQwvrslAgQIEOgs4Duys4u9BAYhUIf7sQ51GIS9MgnMRoFGzGEao0gjxZdXBEM/+MEPpk033TRfcf4xj3lMOuaYY9Kuu+6aB0hjhflIK620UlpttdXy7SIgmr9p+aPYH79FijSdPC2XG7O5YMGCfN/y5cvHHCt2FHVYddVV811V5SnK90qAAAECBAgQIECAAAECBAgQIEBgNgk0YoTpWmutlSKoGYHHWNgpHsVvTwcffHCKhaGuvfba8lAEU2Nu0JgjtFMq9hdByjhnOnk6Xbu4VrxO9Hhyex2i/Cry5IVM8Y911llnijmcPlsE4jNeTCexaNGiNH/+/NnSdO0kUDuB+HuleAw//v5cffXVa1dHFZopgatm6sKuS6CxAuuvv/4Mtc39OEOwLttggZm7HxuMpmkECExboBEjTKP1RSCx02rzcXzDDTeMl3TTTTflr/FHkacISpYHHtkoApkR4CnSdPIUedtfJ7tWnN9eh6rytNfVewIECBAgQIAAAQIECBAgQIAAAQKzQaAxAdNidOPvf//7jv1255135vtjpfkiFXl++9vfFrtGvRb7t9lmm3L/dPKUmds2imvFKNdiWoHWU2JxnL/+9a/5VABbbrllfqiqPK31sE2AAAECBAgQIECAAAECBAgQIEBgtgg0JmC677775n122WWXdey7K6+8Mt+//fbbl8eLPN/61rfKfcVGLExz0UUX5W932mmnYneaTp4yc9tGjIbdeuut80cjL7300rajKV188cX5vKxxTvHIZFV5xlTGDgIECBAgQIAAAQIECBAgQIAAAQKzQKAxAdNnPetZafHixemKK65Ip59++qiu+81vfpPOOuusNHfu3PTUpz61PLb77rvni0Ndd9116bzzziv3x8YZZ5yRbr/99rTJJpukJz/5yeWx6eSJzJdcckm68MIL04033lheKzZe9KIX5e8/85nPjJpL9dZbb01nnnlmfuyQQw7JX4s/qspTlOeVAAECBAgQIECAAAECBAgQIECAwGwRmDOSpaY0NoKS7373u/PFn3bYYYf0pCc9Kd12223p/PPPT7Ha/Fvf+tb07Gc/e1RzI8873/nOfCTn3nvvneLR96uvvjr96Ec/yheP+uhHP5riWq1pOnme85znpJgW4Mgjj0ytAdCHHnooHXHEEemaa65JMXo06vDggw+mGPUaAdsI8J5wwgn5Y/lFHarKU5TnlUCvAhZ96lVQfgL9E7DoU/8sh+1K6517/rBVWX0JDFzg5oMOnJE6uB9nhNVFGy4wU/djw9k0jwCBaQo0KmAaBtdff31asmRJuvbaa1MRC95iiy3S85///PS85z2vI1M8rh9ByZtvvrk8vummm6Y3vOENaeeddy73tW5MNc94AdO4ZgRzTzzxxHTBBReUc5nGaNi//du/Ta95zWs6riheVZ7WNtsmMF0BAdPpyslHoP8CAqb9Nx2WKwrQDEtPqWedBGYqQON+rFMvq8uwCMzU/Tgs7VdPAgSqFWhcwLTgu++++1Is2rThhhum1lXui+OdXmNEZyzAFAsrrbfeeqNGdXY6P/ZNJU88Sh8B0L322qvj5WJkaUwfEIHejTbaKC1YsKDjea07q8rTWqZtAlMVEDCdqpjzCcycgIDpzNnW/coCNHXvIfWro8BMBWjcj3XsbXWqu8BM3Y91b7f6ESAwGIGVB1PszJcaiyRtt912Uyoo5kCNn6mkbvNEIPaPf/xj2nbbbce9/Morr5y22mqrcY93OlBVnk5l20eAAAECBAgQIECAAAECBAgQIECgaQKNWfSp7h3ztre9LR1++OFp7bXXrntV1Y8AAQIECBAgQIAAAQIECBAgQIDArBVo7AjTuvVrUNyaAABAAElEQVRoLB411dGrdWuD+hAgQIAAAQIECBAgQIAAAQIECBBouoARphX1sGBpRdCKIUCAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLAEB02b1p9YQIECAAAECBAgQIECAAAECBAgQINCDgIBpD3iyEiBAgAABAgQIECBAgAABAgQIECDQLIGVm9UcrRmkwLJly9LIyMggq6Dsmgo89NBDZc2WL19ebtsgQKB6gQcffLAsdMWKFemBBx4o39sgQIAAgdECviNHe3hHYJACdbgfV1pppTRv3rxBMiibAIGKBARMK4KeDcXcddddqTUwNhvarI1TF1i6dGmKH4kAgcEL3HfffSl+JAIECBDoLHDHHXd0PmAvAQKVC9Thfoxg6eLFiytvuwIJEKheQMC0evPGlugvjsZ2bc8Nu/fee8ugzFprrZVWXXXVnq/pAgQITE+g9ZcWa665Zpo/f/70LiQXAQIEZoHAOuusMwtaqYkEhkOgDvfjnDlzhgNLLQkQ6FlAwLRnQhcoBObOnVtseiUwSqD1HxbxGIvPyigebwhUKtB6P8a2+7FSfoURIDBkAr4jh6zDVLfRAu7HRnevxhGonYBFn2rXJSpEgAABAgQIECBAgAABAgQIECBAgMCgBARMByWvXAIECBAgQIAAAQIECBAgQIAAAQIEaicgYFq7LlEhAgQIECBAgAABAgQIECBAgAABAgQGJSBgOih55RIgQIAAAQIECBAgQIAAAQIECBAgUDsBAdPadYkKESBAgAABAgQIECBAgAABAgQIECAwKAEB00HJK5cAAQIECBAgQIAAAQIECBAgQIAAgdoJCJjWrktUiAABAgQIECBAgAABAgQIECBAgACBQQkImA5KXrkECBAgQIAAAQIECBAgQIAAAQIECNROQMC0dl2iQgQIECBAgAABAgQIECBAgAABAgQIDEpAwHRQ8solQIAAAQIECBAgQIAAAQIECBAgQKB2AivXrkYqRKBHgTuWL+/xCrL3W+CeFSvSfQ8+mF92Tra96kp+V9Nv416vt2jevF4vIT8BAgQIECBAgAABAgQIEGiEgIBpI7pRI1oFtrngota3tgkQ6ELg5oMO7OIspxAgQIAAAQIECBAgQIAAgeYLGObV/D7WQgIECBAgQIAAAQIECBAgQIAAAQIEuhQQMO0SymkECBAgQIAAAQIECBAgQIAAAQIECDRfQMC0+X2shQQIECBAgAABAgQIECBAgAABAgQIdCkgYNollNMIECBAgAABAgQIECBAgAABAgQIEGi+gIBp8/tYCwkQIECAAAECBAgQIECAAAECBAgQ6FJAwLRLKKcRIECAAAECBAgQIECAAAECBAgQINB8AQHT5vexFhIgQIAAAQIECBAgQIAAAQIECBAg0KWAgGmXUE4jQIAAAQIECBAgQIAAAQIECBAgQKD5AgKmze9jLSRAgAABAgQIECBAgAABAgQIECBAoEsBAdMuoZxGgAABAgQIECBAgAABAgQIECBAgEDzBVZufhO1kAABAgQGJXDH8uWDKlq54wjcu2JFWvrgg/nRkWx7mT4aR2pwuxfNmze4wpVMgAABAgQIECBAgEASMPUhIECAAIEZE9jmgotm7NouTKCpAjcfdGBTm6ZdBAgQIECAAAECBIZCwCP5Q9FNKkmAAAECBAgQIECAAAECBAgQIECAQBUCAqZVKCuDAAECBAgQIECAAAECBAgQIECAAIGhEBAwHYpuUkkCBAgQIECAAAECBAgQIECAAAECBKoQEDCtQlkZBAgQIECAAAECBAgQIECAAAECBAgMhYCA6VB0k0oSIECAAAECBAgQIECAAAECBAgQIFCFgIBpFcrKIECAAAECBAgQIECAAAECBAgQIEBgKAQETIeim1SSAAECBAgQIECAAAECBAgQIECAAIEqBARMq1BWBgECBAgQIECAAAECBAgQIECAAAECQyEgYDoU3aSSBAgQIECAAAECBAgQIECAAAECBAhUISBgWoWyMggQIECAAAECBAgQIECAAAECBAgQGAoBAdOh6CaVJECAAAECBAgQIECAAAECBAgQIECgCgEB0yqUlUGAAAECBAgQIECAAAECBAgQIECAwFAICJgORTepJAECBAgQIECAAAECBAgQIECAAAECVQgImFahrAwCBAgQIECAAAECBAgQIECAAAECBIZCQMB0KLpJJQkQIECAAAECBAgQIECAAAECBAgQqEJAwLQKZWUQIECAAAECBAgQIECAAAECBAgQIDAUAgKmQ9FNKkmAAAECBAgQIECAAAECBAgQIECAQBUCAqZVKCuDAAECBAgQIECAAAECBAgQIECAAIGhEBAwHYpuUkkCBAgQIECAAAECBAgQIECAAAECBKoQEDCtQlkZBAgQIECAAAECBAgQIECAAAECBAgMhYCA6VB0k0oSIECAAAECBAgQIECAAAECBAgQIFCFgIBpFcrKIECAAAECBAgQIECAAAECBAgQIEBgKAQETIeim1SSAAECBAgQIECAAAECBAgQIECAAIEqBARMq1BWBgECBAgQIECAAAECBAgQIECAAAECQyEgYDoU3aSSBAgQIECAAAECBAgQIECAAAECBAhUISBgWoWyMggQIECAAAECBAgQIECAAAECBAgQGAoBAdOh6CaVJECAAAECBAgQIECAAAECBAgQIECgCgEB0yqUlUGAAAECBAgQIECAAAECBAgQIECAwFAICJgORTepJAECBAgQIECAAAECBAgQIECAAAECVQgImFahrAwCBAgQIECAAAECBAgQIECAAAECBIZCQMB0KLpJJQkQIECAAAECBAgQIECAAAECBAgQqEJAwLQKZWUQIECAAAECBAgQIECAAAECBAgQIDAUAgKmQ9FNKkmAAAECBAgQIECAAAECBAgQIECAQBUCAqZVKCuDAAECBAgQIECAAAECBAgQIECAAIGhEBAwHYpuUkkCBAgQIECAAAECBAgQIECAAAECBKoQEDCtQlkZBAgQIECAAAECBAgQIECAAAECBAgMhYCA6VB0k0oSIECAAAECBAgQIECAAAECBAgQIFCFgIBpFcrKIECAAAECBAgQIECAAAECBAgQIEBgKAQETIeim1SSAAECBAgQIECAAAECBAgQIECAAIEqBARMq1BWBgECBAgQIECAAAECBAgQIECAAAECQyEgYDoU3aSSBAgQIECAAAECBAgQIECAAAECBAhUISBgWoWyMggQIECAAAECBAgQIECAAAECBAgQGAoBAdOh6CaVJECAAAECBAgQIECAAAECBAgQIECgCoGVqyhEGQQIECBAgAABAgQIECBAgAABAoMRGLn33sEUPMVS5yxcOMUcTicwMwICpjPj6qoECBAgQIAAAQIECBAgQIAAgVoILH3da2pRj8kqsfCzZ0x2iuMEKhHwSH4lzAohQIAAAQIECBAgQIAAAQIECBAgQGAYBARMh6GX1JEAAQIECBAgQIAAAQIECBAgQIAAgUoEBEwrYVYIAQIECBAgQIAAAQIECBAgQIAAAQLDICBgOgy9pI4ECBAgQIAAAQIECBAgQIAAAQIECFQiIGBaCbNCCBAgQIAAAQIECBAgQIAAAQIECBAYBgEB02HoJXUkQIAAAQIECBAgQIAAAQIECBAgQKASgZUrKWWAhVx55ZXp4x//eHryk5+c/uEf/qFjTZYtW5bOPvvsdPnll6c77rgjbbnllmmnnXZKBx54YJo7d27f8nS80CM7f/WrX6UvfvGL6YYbbkgLFixI22+/fdpnn33SZpttNm62qvKMWwEHCBAgQIAAAQIECBAgQIAAAQIECDRMoNEB03vuuSe9973vTbfeemvaaKONOnbdnXfemV772temP/zhD/nxRz/60en888/Pf374wx+mY489Ns2bN29U3unkGXWBtjcRrD3ppJPyvQsXLkzLly9PP/3pT9MXvvCFtGTJkrTzzju35Uh5gLeKPGMKtoMAAQIECBAgQIAAAQIECBAgQIBAgwUa/Uj+hz/84TxYOlH/HXfccXmwNEagnnvuuemrX/1qOuuss9Lmm2+eLrnkkvSxj31sTPbp5BlzkUd2XH311XkZEZQ9/vjj0ze+8Y08WHvkkUem+++/Px199NHp5ptvHpW9qjyjCvWGAAECBAgQIECAAAECBAgQIECAwCwQaGzA9IILLkjf/va306Me9ahxu/GXv/xl+slPfpJWW221fCTqWmutlZ+74YYbpo985CP54/jnnXdeipGqRZpOniJvp9fPfvazaWRkJB122GFpzz33THPmzEmrrLJKOuSQQ9ILXvCCtGLFinTOOeeMylpVnlGFekOAAAECBAgQIECAAAECBAgQIEBgFgg0MmAaIzIj4BmBzxe96EXjduN3vvOd/NjTn/70NH/+/FHnxaP5u+22W/54fARNizSdPEXe9tf77rsvD9jG/gMOOKD9cLkvRr4++OCD+fGq8oypjB0ECBAgQIAAAQIECBAgQIAAAQIEZoFA4wKmDz/8cD5aNB5nf8c73jEmENrap7/4xS/yt/E4fqcUAdNIP//5z8vD08lTZm7buOaaa/LRpTG/6gYbbNB2NKWtt946rbHGGumuu+5KN954Y368qjxjKmMHAQIECBAgQIAAAQIECBAgQIAAgS4F4qnpWKdnGFPjFn363Oc+l6666qr08pe/PG277bbp17/+9bj98qc//Sk/Nt5j+8X+YkGoOHk6ecarwGTXinxRh5gSIOqw2WabTVp+v/KMV+eJ9seNIBEgMJwC7t/h7De1bqaA+7GZ/apVwyngfhzOflPrZgrU4X6MKfRWXrlxYZRGfWDOOOOMdOKJJ06pTY997GPHTIU4pQtkJz/00EP5QLfHPe5xU8066vxTTz01/fu//3v+tPSb3vSmUcc6vbn22mvTi1/84k6H8gXMI6b0mMc8Jj3nOc9Jz33uc9Oqq67a8dx+77z44ovTu9/97vyp6gceeCA9+9nPztsUT4NHPd75zneOKvK6665LW2655ah9g37TqDs9gqOf+tSn0lZbbZVe8YpXTGq7dOnS/JwiMNqeYc0118x3FefFm2J7Knnar1u8n+xacV57HarKU9RxKq933HFH/iUxlTzOJUCgHgK33XZbPSqiFgQIJPejDwGB+gi4H+vTF2pCoA73YyzWvHjxYp1RY4GYovGKK66YUg0jltFLuvzyy9OrXvWq9Pd///fpmGOO6eVS6c9//nNe/z322KOr68S0jd2097/+67/Suuuum77//e+nLbbYoqtrT/ekP/7xj+nv/u7vUrjGLxgiRrfNNtukW265Ja/rDjvsUF46As1vf/vb0ymnnDJq/aDyhAFuNCZgumzZsvSe97wnX6gpHsWf7Lc+8eh+RLkjxWPvndLChQvz3XHtSNPJk2cc54/4YEcar/w4VtShqGtVeaJsiQABAgQIECBAgAABAgQIECAwLAIve9nL0n777TemunvvvXcewPvoRz+a9tprr1HHex11GQtzx1SOETAdZLr00ktHjSCNBcYjhvTjH/84n7IyApYxuvOyyy5LCxYsmLGq/vCHP8yt11tvvRQLpy9atCgvK9YEev3rX59ap8W8884705IlS/LRsDNWoWleuDEB049//OP58Od/+Zd/SZtsssmkHCuttFJabbXVUsx1WgRE2zMV++O3SJGmk6f9mq3viw/oRPM5FHUobuCq8rTWs9vtqGMElSUCBIZPoH3hu+FrgRoTaI6A+7E5faklwy/gfhz+PtSC5gjU4X5cZZVVmgPa0JasvfbaKX7aUzGoLh6Z33HHHdsPN+J9jNzsdJ885SlPSbHY+a677ppiXZzvfe976cADD5yxNhfTT0ZgugiWRmHxPn6GJTUiYPqjH/0ofeUrX8k7P4b9dptiHoeYGzTmCO2Uiv1FkDLOmU6eTtcurhWvd99993inlHUr6hDlV5Fn3ApNcGCttdaa4KhDBAjUWaD1L7I611PdCMwGAffjbOhlbRwWAffjsPSUes4GAffjbOjl+rQx5syNUaMxQjKCrRGMjEfLi8Br1DQeJ//Vr36V/vrXv+YVjxGcsVB4xG3i8ffWFI/ax7nXX399Pq1DzNcZC30XA/Raz52J7V122SWvf0xlGTG0ImBatO/xj398+v3vf58HU5/61Kfma+i01qMbj3vvvTfdcMMNKR7JjxQjXMMjBh/GI/m33357iikT4l6Ohc9vuumm9Nvf/nbUufEmzo08g06NCJiec845uWN0dAwvbk3FCM3vfve7+SS3EdT77//+7/yUyYKfRSCz9Yt5Onla69O6XQQ/i8Bs67Fiu70OVeUpyvdKgAABAgQIECBAgAABAgQIEJgtAuedd1466qijUiyo1JoiwBmP3++222757ggAbrfdduUpJ510UoqfWKzpQx/6UL4/goevfvWr0ze/+c3yvGJj8803T6eddlrac889i10z+rrxxhvnC6O3xqCe+MQnpnh0PqYqOOSQQ8p1aT784Q+nN77xjXl9uvWI+VGf+cxnlm34/Oc/n+Innu6OqQFOP/30/JqvfOUr83Yfd9xx6ZOf/GR+fgRkC8uoXzE9ZXmxAWw0ImA6d+7cfO7SCI4WAdLCMiLakR588MF8tGacW6R11lkn34yI9u67717sLl+LSHdEt4s0nTxF3vbX4loxyjU+HO3D+++66678NxURWS9WC6sqT3tdvSdAgAABAgQIECBAgAABAgQINFkg5tmMFd0jlnTooYemgw46KJ96MAbqxZPN8Xj7+eefn8+TGuvRRKDxS1/6Uj4yM/I94xnPSDvvvHNOFPNzxsjUWPxor+xR9P333z9FkDRWkD/33HPTb37zm3xxpBtvvDEPKs6ka8SXisWhisBkUV4EKP/hH/4hf5x/p512ys8rAp9T8YjYWXhcdNFF6Wtf+1qKkaoRhG0dlVuUGa9x7LGPfWz613/91zymF0HaSMWUlPmbAf4x+DGufWj8CSeckKITO/3EnKaR9t133/x4MRq12Bev3/rWt+JlVIq5OKOTI8UHpkhxnUhTyVPkbX+NIcjxG4oYthyT87anuIliiHecs/rqq+eHq8rTXhfvCRAgQIAAAQIECBAgQIAAAQJNFYjAYYx+jGBpBO/OOuusdNhhh6VYSOrLX/5yete73pXHaF772tfmg/Vi5GTEnIo5USNAGO+f9rSn5USf/vSn82Dp9ttvn8eQjjnmmPTCF74wH1V5+eWX54+d33bbbR1Hn/bTOOYtfcELXpAPyFu8ePGYJ7MjoBvB35h7NEaJxvsIfk7VI9YTivbvscceefWj3fH+da97XcfmxEJcr3nNa/JjMbgxzo2f9sGEHTNXsLMRAdPpOsWo0k033TRdd911KYYYt6Yzzjgjn18hOrx1Ba/p5InrXnLJJenCCy/MF6ZqLedFL3pR/vYzn/lMOV9p7Lj11lvTmWeemR+LqHtrqipPa5m2CRAgQIAAAQIECBAgQIAAAQJNFYiRozGPZwxai8Bde3rb296WYtGoiCF9/etfbz885n0Mvjv++OPTqaeemo+gbD0h5jiN0aqRijlQW49PZztiV/GIffGz7bbbpkc/+tHpCU94Qh6wjUDkxz72sXyO1fbrv/Wtb03FujTFwlH99mgvs+7vG/FI/nSR58yZkw87fuc735lilGpMfBuPvl999dX5dnyY3vKWt6Q4r0jTyRN5P/jBD6YYjn3kkUemmDeiSLFSWUTuI+If81pEhD2mD4gRrDEfRvyGYp999ilOz1+ryjOqUG8IECBAgAABAgQIECBAgAABAg0ViLhMpP32229MgDP2R4woYja/+93v8hhO7JsoxRPKxVPKcV48QRx5Y+GlH//4x/kj+bE/YkD9SLFIVXuKGFbMURqD/5YsWZIv/NR+TryPIHF76rdH+/Xr/n5WB0yjc2Jy3RNPPDEPmMYj8PETadNs5Okb3vCGfL6JfEfLH9PJ05J91GYMOz755JPzOlxwwQUpRrZGiv0xZDqGJ7evDlZVnlEV9YYAAQIECBAgQIAAAQIECBAg0FCBIkAY8aDxUowwjRSjTLtJsWp8zOv57W9/Ow+ULl++vMw23tye5QlT3Ig5Slvn/4ztmCO0GDE60eU222yzMYdnwmNMITXe0fiA6cEHH5ziZ6IUw5W/+MUv5iM6YwGmWFgpIvDtgcrWa0w1TwzXjkfp11577dbL5NvxIY7hz0cffXT+G4aYL2OjjTZKCxYsGHNusaOqPEV5XgkQIECAAAECBAgQIECAAAECTRUo5s6MRbnHS/fff39+qDh3vPNifwQwY7GnWLdm3rx5aZdddkm77rprPjAvRqrGKvSxOFK/Ujx6301wtFN5rYHW4njRxn55FNcdltfGB0yn0hEx+W38TCV1mycCsX/84x9TzCExXorfLmy11VbjHe64v6o8HQu3kwABAgQIECBAgAABAgQIECDQAIGYojFSzGM6XooRo5FioN1kKRaQimDpi1/84vSpT30qxSJRrSkez48Ui47XMfXbo45tnKhOs3rRp4lg+n0sJgc+/PDDO44w7XdZrkeAAAECBAgQIECAAAECBAgQINC9wHbbbZefHKM+i5GkrbnvuuuucsHwmKqxSMXTyTFHaZFiIadYHyfSu9/97jHB0qVLl6Zrr702P96vOUzzi/Xxj+l6TKUKhV0dg8YCplPpyR7OjTkr4rcLEgECBAgQIECAAAECBAgQIECAQL0Enve85+UrzN900035lImtAdB4LP2oo47KV7SPJ4djYagiFSNHi9GnsX/11VdPxRylxVo5xfkPPPBAeu5zn5uWLVuW74r3dUzT9ZhKW8IpUgSN//SnP00l64yf65H8GSf+fwVM9VH/iqqlGAIECBAgQIAAAQIECBAgQIDArBeI0Y6f+MQn0v7775+/xkr2Bx54YP7I/De+8Y0Uq9DHavKxgFMxv2egbb755rndaaedli677LI8GPqe97wnHXroofnC3rFezXe/+920xx575Ne48MILU0zbGPOZXn755bULFBYfhOl6FPm7eY25UzfYYIP05z//OQ9Wb7zxxinWAFp//fW7yT6j5xhhOqO8Lk6AAAECBAgQIECAAAECBAgQIDAMArvvvnv+KP0BBxyQBzdPOOGEtGTJknxk6Utf+tJ00UUXpXXXXXdUU17xilekQw45JB9RetVVV+XnxAkRfC3mMT3jjDPSEUcckU499dQ8wBqP65944on5dc4555wUi3/XMU3HY6rtOP300/Og6V/+8pd8oaxf/OIXU73EjJw/J+uUevbKjDTXRWeDwHrnnj8bmqmNBPoqcPNBB/b1esXF3I+FhFcC3Qu4H7u3ciaBmRZwP860sOsT6F5gpu7H7msw3Gfe+/KXDEUDFn72jNrUMx6Zv+aaa9LChQvTFltsMWm9li9fnuJx/hgdOW/evPL8O++8M11//fV5QHWbbbZJnVakL0+u8cZUPabalJtvvjkfuVuXJ7Q9kj/VHnQ+AQIECBAgQIAAAQL/l737gLKjqv8AfkMCJKRDQAi9RIIhlEhTekC6iggigihIUTwgKBbQv9jgUEQMqIAgTemgINWAVBHpTUxoAgklIC20kEb+/K6+PS9bkmze282beZ85Z7Pzpty593MzKd+9c4cAAQIECBAotUAEm+uss848tzFC0hVXXLHN8YMGDcqP37fZUbANnfXobPOWXnrpzp7Spcd7JL9LeRVOgAABAgQIECBAgAABAgQIECBAgECRBASmReotdSVAgAABAgQIECBAgAABAgQIECBAoEsFBKZdyqtwAgQIECBAgAABAgQIECBAgAABAgSKJCAwLVJvqSsBAgQIECBAgAABAgQIECBAgAABAl0qIDDtUl6FEyBAgAABAgQIECBAgAABAgQIECBQJAGBaZF6S10JECBAgAABAgQIECBAgAABAgQIEOhSAYFpl/IqnAABAgQIECBAgAABAgQIECBAgACBIgkITIvUW+pKgAABAgQIECBAgAABAgQIECBAgECXCghMu5RX4QQIECBAgAABAgQIECBAgAABAgQIFEmgV5Eqq64ECBAgQIAAAQIECBAgQIAAAQKdFOjbt5MnOJxAcwsITJu7/7WeAAECBAgQIECAAAECBAgQKLlAv9/8tuQt1DwC9RXwSH59PZVGgAABAgQIECBAgAABAgQIECBAgECBBQSmBe48VSdAgAABAgQIECBAgAABAgQIECBAoL4CHsmvr6fSCBAgQIAAAQIECBAgQIAAAQINJfD6tGkNVZ+OKjN4kUU62mU7gW4VEJh2K7eLESBAgAABAgQIECBAgAABAgS6V2CNsTd17wXn82qTdtpuPs90GoH6Cngkv76eSiNAgAABAgQIECBAgAABAgQIECBAoMACAtMCd56qEyBAgAABAgQIECBAgAABAgQIECBQXwGBaX09lUaAAAECBAgQIECAAAECBAgQIECAQIEFBKYF7jxVJ0CAAAECBAgQIECAAAECBAgQIECgvgIC0/p6Ko0AAQIECBAgQIAAAQIECBAgQIAAgQILCEwL3HmqToAAAQIECBAgQIAAAQIECBAgQIBAfQUEpvX1VBoBAgQIECBAgAABAgQIECBAgAABAgUWEJgWuPNUnQABAgQIECBAgAABAgQIECBAgACB+goITOvrqTQCBAgQIECAAAECBAgQIECAAAECBAosIDAtcOepOgECBAgQIECAAAECBAgQIECAAAEC9RUQmNbXU2kECBAgQIAAAQIECBAgQIAAAQIECBRYQGBa4M5TdQIECBAgQIAAAQIECBAgQIAAAQLVAlOmTKn+aH0+BHrNxzlOIUCAAAECBAgQIECAAAECBAgQINBQAueff3466aSTOlWn5ZZbLl1xxRWdOqf1wTNnzkwTJkxIK6+8cutdnfp8xhlnpNNPPz3tscce6Vvf+tY8nztr1qz061//Ot1www3poYceSs8++2waMmRIWmuttdI666yTyxo6dOg8lzenA9dbb728+5Zbbkn9+vWb06GF3icwLXT3qTwBAgQIECBAgAABAgQIECBAgEAITJo0Kd13332dwnj99dc7dXzrg++99970la98JX3+859PRxxxROvdnfr8wgsv5Ppvsskm83zem2++mfbee+905ZVX5nN69uyZPvShD6XJkyenm266KX9deOGF6Y9//GPaaKON5rncjg6s+M6YMaOjQ0qx3SP5pehGjSBAgAABAgQIECBAgAABAgQINLdABIcPPvhgm6/BgwdnmF/+8pdt9l1zzTU1oZ177rnp4YcfrqmMWk6OsDbC0hEjRqSxY8emqVOn5uA4HssfN25c2nXXXdOLL76YNt988/T444/XcqmmOtcI06bqbo0lQIAAAQIECBAgQIAAAQIECJRTYMkll0zx1Xrp1eu/8Vc8Mr/22mu33l3Yz++991667rrr0kILLZSuvfbatMIKK8zWluHDh6eLL744jR49Ot16663p8ssvr3kU7GwXKPEHgWmJO1fTCBAgQIAAAQIECBAgQIAAAQIEOicwffr0PGr0X//6V4qwNeYCXX311fN6paSYt3T8+PHptddey5teeuml9Oijj+a5Q+OR+OolHrWPY5988sm0xBJLpGHDhqUIMxdZZJHqwzq9fv/996d33nknl7nsssu2e36Eqfvuu28OTK+//vp2A9MIXmP06WOPPZYf5V911VVz/ZZZZpl2y+xo4/vvv5/LiXlU45H9mD812hnTBHS0hNs999yTnn/++Rz4fuQjH0krrrhiR4d323aBabdRuxABAgQIECBAgAABAgQIECBAgEAjC8SIzUMPPbTN4+sR/MXj9xtssEGu/quvvprWXHPNlqaMGTMmxVe8rOnnP/953v7cc8+l/fbbL/3lL39pOa6yEqHkWWedlTbbbLPKpk5/j+svvPDCKeoSc5Tutttu7ZbxhS98IT+av9hii7XZ/7vf/S5985vfTDEXavUSQetBBx2Ujj/++NSnT5/qXe2uR1i855575pdOVR8QdYyXcUXo3Ho5/PDD0ymnnJKmTZs2265tt902nX322amzge1shdT4QWBaI6DTCRAgQIAAAQIECBAgQIAAAQIEii8Qb37fcccdU7x1fvfdd0877bRTilGTV1xxRfrTn/6UPv7xj6cYpbn11lun/v37p5gTNR5zv/322/N5n/jEJ9KoUaMyxBtvvJFDwnip1BZbbJG22WabFCHpzTffnK6++ur01FNPpc9+9rNpwoQJ8xRItqc7YMCAtP3226c///nPOayMusWcpTFfaXU4GqNkK9MSVJfzk5/8JB111FF5VOyBBx6Y4mVT//nPf3L9brvttvSrX/0qj/aMYHNOS4wQjeA3RqpGm3bZZZc8TUDUpxIy33HHHemjH/1oSzEnnnhiiq8w+fa3v51Hlz7wwAN5CoEImHfYYYcUnxfU4qVPC0redQkQIECAAAECBAgQIECAAAECBBpC4K233kr77LNPDksjyLvooovSXnvtld9AH6M3f/SjH6V4DD9GXcaLlWLU5Te+8Y2WOVE33njj/HnTTTfN7YmRmxGWjhw5Mt144435UfjPfe5z6dRTT0333ntvDhRfeeWVdkefdgYk6rnzzjunmEYgRqxG0Lj44ovnkDbqfPfdd7dbXISbEYjG8pvf/Caddtppub2HHXZY+utf/5oiQI3lD3/4Q/7e0S8RLh9yyCE5LP3xj3+cLrvsshQjWj//+c+nc845J4+2Da84pnqJesdy5pln5mtF8HvkkUdmj759+7a8nKv6nO5cF5h2p7ZrESBAgAABAgQIECBAgAABAgQINJxAjIZ85pln8pybEYS2XiLMi5dGPfHEE+mqq65qvbvN55i/8+ijj05nnHFGmzk8Y47TGK0aS2UO1DYFzOOGCG5jlOt5552XX+4U86JGQBkveYoAc8MNN8wjXWMO1eol5j6N/cccc0y7j/J/5jOfyYfPrX4xKvcf//hHijlUv//971dfIq8fcMABqXfv3unvf/97Pq5yQMxxGkvrei299NJ5ZGnMbRqGC2rxSP6CknddAgQIECBAgAABAgQIECBAgACBhhAYN25crkc8bt/eS4pirtAtt9wyPf3006ly7JwqvtVWW6X4qiwxOjXOjRcrRcAYj+THUgkOK8fNz/eYb/SLX/xi/oogNMLSePR/7Nix+eVVjzzySJ57NUaLfupTn8qXiJdPfe1rX5vtcjEiNl7+FMfHtAGxzK1+8WKsWFZaaaU8NUH+0OqXmMc0RtVGOLrRRhvlvRHIPvjgg7kOMV9pjIyNUabrrbdefilWqyK6/aPAtNvJXZAAAQIECBAgQIAAAQIECBAgQKCRBCohaAR/HS0xwjSWGGU6L8uzzz6b5zmNR9wjKK1+uVF7c4rOS5lzOyYeZ4/wMb5OOOGE/Ab6r3zlKzkEjdGen/zkJ1OPHj1yMRHixujUmD7g4YcfTpMmTWopfl7rV7GIOUojUJ7T8uSTT7bs/r//+78U0yD84he/yNMGxNQBMYXAkksumedBjf1Dhw5tOb67VwSm3S3uegQIECBAgAABAgQIECBAgAABAg0lECNIY4m5QDtapkyZkndVju3ouNh+33335XlE33777RSPyccLj2L0ZLwtPoLFeDN9vKypluW3v/1tilA25l5dbbXV2i1q/fXXzyM/YxqAeMw96hX1iLlH995773TBBRfk85Zbbrn8aP7aa6+d98cLpSrTBrRb8P82RjmxRJsijJ3Tsu6667bsjtA2At1DDz00xRyx8aKnGBUbL52K+VTjRVs33HBDitGpC2IRmC4IddckQIAAAQIECBAgQIAAAQIECBBoGIFhw4blusQ8ph0tEU7GstRSS3V0SMv2CDEjLI0XIMWLjWKu0eolHs+P5f3336/e3Kn1008/Pd1///35BVI//elPOzx34MCBOXiMsDTaF4Hpn/70pxyWxr4IbuMt99VLhJixzK1+H/7wh/Nx/fv3T/HCqM4uMffpwQcfnL9i7tUISb/61a+m559/Ps//OmbMmM4WWZfjvfSpLowKIUCAAAECBAgQIECAAAECBAgQKKpAZSRjhIeVkaTVbZk8eXK67rrr8qbqcDHmD40lHm+vLPGipJgHNJZ4sVLrsDTmGY25QmOZ2xyh+aAOfhk9enTeE3OTvvjiix0cldLLL7+c5wuNx+xjjtZYYp7TWHbaaac2YWlsf+CBB+LbXOtXcbvtttvSK6+8ks+p/uXdd9/NL5762Mc+lkeQxr6JEyemzTffPEXYGq6VZdFFF831Ofzww/Omhx56qLKr278LTLud3AUJECBAgAABAgQIECBAgAABAgQaSeDTn/50ikfGI3iMwK46AI3H9OPR8QhCR4wY0RI6Rv0rYWhl9GlsW2yxxVJlDtB4zLx6ee+99/KLl2I0ZSzxeX6XqFO8vClGjY4aNSpdc801bcqLF0xtsskmuT0RsA4aNChfLh65j+X2229vE4pee+216bjjjsv751a/CD632GKL9MYbb6QDDzywTdj8ve99L89R+s9//jNPSxCFxuP/8dKrmP/0xBNPzNep/qUSTFcC4ep93bXukfzuknYdAgQIECBAgAABAgQIECBAgACBhhSIkaK/+c1v0jbbbJO/R9C43Xbb5UfSI0CMlyINHz48xQucqucwXXXVVXN7zjrrrPyCpXgL/U9+8pO0++67p/PPPz+HrzGaM0LLKCMeOY8RlvFYfLw5Ph49n98lHmePlyXFNR999NE8OrNnz55pjTXWSPGIfLzBvjKCM0aWXnTRRS2XivrFHKITJkzII0B33nnnFCM8o67RxnXWWSfXN0bDRhnx6H5Hy69+9as8YjQe4485UKM+YXTVVVfleoVtvFiqEtJW5i+N6QpiKoFLL7001z1Go44dOzbFy6FWXHHFPMdqR9fs6u0C064WVj4BAgQIECBAgAABAgQIECBAgEDDC2y00Ub5UfoYKRmhYcwPGkuMiPziF7+YR13Gy5Oqly9/+cs5BL3yyitTPELer1+/HJhG+Bovezr33HNzcBrhaYw63WqrrVIEsPECpk033TS/3CjeFF95c3112fOyvsoqq6Q777wzffe7301///vf0/jx41OM5owlwtORI0fmlzHF1ACVUa+xLx6lv/zyy/PcodHOSltjftYIUr/+9a/n8DgcYr7TaGdHS4y6jcA2zomQtHrUaIzaPfroo9P2228/2+l77LFH/nzUUUflOke9Y4nRuRG4RsA6ZMiQvG1B/NLjg7dZ/fd1Vgvi6q5JoAsElr76+i4oVZEEyi0waaftuqSB7scuYVVoyQXcjyXvYM0rlID7sVDdpbIlF+iq+7HkbC3NK8q/yxupn+OR+XHjxuUAtKM30LcAf7Aybdq0/Dj/Msssk4PSyr54VD1GTEZYGSM/YxRnVy4xJ2o86h6jQtdaa60cQM7pejH1QEwn8MILL6QYLRv1r2WpXD/avfLKK6ell156jsVFLBnXjpGuMV3A6quvnl9iNceTumGnEabdgOwSBAgQIECAAAECBAgQIECAAAECxRGIYDMeS5/XJUaTxmPkrZcIAePx++5aKsHsvF4vRqHGKNX4qsfS2evHyNqYWiC+Gmnx0qdG6g11IUCAAAECBAgQIECAAAECBAgQIEBggQoITBcov4sTIECAAAECBAgQIECAAAECBAgQINBIAgLTRuoNdSFAgAABAgQIECBAgAABAgQIECBAYIEKCEwXKL+LEyBAgAABAgQIECBAgAABAgQIECDQSAIC00bqDXUhQIAAAQIECBAgQIAAAQIECBAgQGCBCghMFyi/ixMgQIAAAQIECBAgQIAAAQIECBAg0EgCAtNG6g11IUCAAAECBAgQIECAAAECBAgQIEBggQoITBcov4sTIECAAAECBAgQIECAAAECBAgQINBIAgLTRuoNdSFAgAABAgQIECBAgAABAgQIECBAYIEK9FqgV3dxAgQIECBAgAABAgQIECBAgACBLhUYtPDCXVq+wgmUTUBgWrYe1R4CBAgQIECAAAECBAgQIECAQJXA+G23qvpklQCBuQl4JH9uQvYTIECAAAECBAgQIECAAAECBAgQINA0AgLTpulqDSVAgAABAgQIECBAgAABAgQIECBAYG4CAtO5CdlPgAABAgQIECBAgAABAgQIECBAgEDTCAhMm6arNZQAAQIECBAgQIAAAQIECBAgQIAAgbkJCEznJmQ/AQIECBAgQIAAAQIECBAgQIAAAQJNIyAwbZqu1lACBAgQIECAAAECBAgQIECAAAECBOYmIDCdm5D9BAgQIECAAAECBAgQIECAAAECBAg0jYDAtGm6WkMJECBAgAABAgQIECBAgAABAgQIEJibgMB0bkL2EyBAgAABAgQIECBAgAABAgQIECDQNAIC06bpag0lQIAAAQIECBAgQIAAAQIECBAgQGBuAgLTuQnZT4AAAQIECBAgQIAAAQIECBAgQIBA0wj0apqWamiXC0yePDm9//77XX4dFyBAoP4Cr7/+ev0LVSIBAvMl4H6cLzYnEegSAfdjl7AqlMB8CTTC/bjwwgunfv36zVf9nUSAQLEEBKbF6q+Grm2EpTNnzmzoOqocAQLtC7h323exlcCCEHA/Lgh11yTQvoD7sX0XWwksCIFGuB8XWshDugui712TwIIQEJguCPWSXnPw4MElbZlmESi/wJAhQ8rfSC0kUBAB92NBOko1m0LA/dgU3ayRBRFwPxako1STQEkE/HikJB2pGQQIECBAgAABAgQIECBAgAABAgQI1C4gMK3dUAkECBAgQIAAAQIECBAgQIAAAQIECJREQGBako7UDAIECBAgQIAAAQIECBAgQIAAAQIEahcQmNZuqAQCBAgQIECAAAECBAgQIECAAAECBEoiIDAtSUdqBgECBAgQIECAAAECBAgQIECAAAECtQsITGs3VAIBAgQIECBAgAABAgQIECBAgAABAiUREJiWpCM1gwABAgQIECBAgAABAgQIECBAgACB2gUEprUbKoEAAQIECBAgQIAAAQIECBAgQIAAgZIICExL0pGaQYAAAQIECBAgQIAAAQIECBAgQIBA7QIC09oNlUCAAAECBAgQIECAAAECBAgQIECAQEkEBKYl6UjNIECAAAECBAgQIECAAAECBAgQIECgdgGBae2GSiBAgAABAgQIECBAgAABAgQIECBAoCQCAtOSdKRmECBAgAABAgQIECBAgAABAgQIECBQu4DAtHZDJRAgQIAAAQIECBAgQIAAAQIECBAgUBIBgWlJOlIzCBAgQIAAAQIECBAgQIAAAQIECBCoXUBgWruhEggQIECAAAECBAgQIECAAAECBAgQKImAwLQkHakZBAgQIECAAAECBAgQIECAAAECBAjULiAwrd1QCQQIECBAgAABAgQIECBAgAABAgQIlERAYFqSjtQMAgQIECBAgAABAgQIECBAgAABAgRqFxCY1m6oBAIECBAgQIAAAQIECBAgQIAAAQIESiIgMC1JR2oGAQIECBAgQIAAAQIECBAgQIAAAQK1CwhMazdUAgECBAgQIECAAAECBAgQIECAAAECJREQmJakIzWDAAECBAgQIECAAAECBAgQIECAAIHaBQSmtRsqgQABAgQIECBAgAABAgQIECBAgACBkggITEvSkZpBgAABAgQIECBAgAABAgQIECBAgEDtAgLT2g2VQIAAAQIECBAgQIAAAQIECBAgQIBASQQEpiXpSM0gQIAAAQIECBAgQIAAAQIECBAgQKB2AYFp7YZKIECAAAECBAgQIECAAAECBAgQIECgJAIC05J0pGYQIECAAAECBAgQIECAAAECBAgQIFC7gMC0dkMlECBAgAABAgQIECBAgAABAgQIECBQEgGBaUk6UjMIECBAgAABAgQIECBAgAABAgQIEKhdQGBau6ESCBAgQIAAAQIECBAgQIAAAQIECBAoiYDAtCQdqRkECBAgQIAAAQIECBAgQIAAAQIECNQuIDCt3VAJBAgQIECAAAECBAgQIECAAAECBAiUREBgWpKO1AwCBAgQIECAAAECBAgQIECAAAECBGoXEJjWbqgEAgQIECBAgAABAgQIECBAgAABAgRKIiAwLUlHagYBAgQIECBAgAABAgQIECBAgAABArULCExrN1QCAQIECBAgQIAAAQIECBAgQIAAAQIlERCYlqQjNYMAAQIECBAgQIAAAQIECBAgQIAAgdoFBKa1GyqBAAECBAgQIECAAAECBAgQIECAAIGSCAhMS9KRmkGAAAECBAgQIECAAAECBAgQIECAQO0CAtPaDZVAgAABAgQIECBAgAABAgQIECBAgEBJBASmJelIzSBAgAABAgQIECBAgAABAgQIECBAoHYBgWnthkogQIAAAQIECBAgQIAAAQIECBAgQKAkAgLTknSkZhAgQIAAAQIECBAgQIAAAQIECBAgULuAwLR2QyUQIECAAAECBAgQIECAAAECBAgQIFASAYFpSTpSMwgQIECAAAECBAgQIECAAAECBAgQqF1AYFq7oRIIECBAgAABAgQIECBAgAABAgQIECiJgMC0JB2pGQQIECBAgAABAgQIECBAgAABAgQI1C4gMK3dUAkECBAgQIAAAQIECBAgQIAAAQIECJREQGBako7UDAIECBAgQIAAAQIECBAgQIAAAQIEahcQmNZuqAQCBAgQIECAAAECBAgQIECAAAECBEoiIDAtSUdqBgECBAgQIECAAAECBAgQIECAAAECtQsITGs3VAIBAgQIECBAgAABAgQIECBAgAABAiUREJiWpCM1gwABAgQIECBAgAABAgQIECBAgACB2gUEprUbKoEAAQIECBAgQIAAAQIECBAgQIAAgZIICExL0pGaQYAAAQIECBAgQIAAAQIECBAgQIBA7QIC09oNlUCAAAECBAgQIECAAAECBAgQIECAQEkEBKYl6UjNIECAAAECBAgQIECAAAECBAgQIECgdgGBae2GSiBAgAABAgQIECBAgAABAgQIECBAoCQCAtOSdKRmECBAgAABAgQIECBAgAABAgQIECBQu4DAtHZDJRAgQIAAAQIECBAgQIAAAQIECBAgUBIBgWlJOlIzCBAgQIAAAQIECBAgQIAAAQIECBCoXUBgWruhEggQIECAAAECBAgQIECAAAECBAgQKImAwLQkHakZBAgQIECAAAECBAgQIECAAAECBAjULiAwrd1QCQQIECBAgAABAgQIECBAgAABAgQIlERAYFqSjtQMAgQIECBAgAABAgQIECBAgAABAgRqFxCY1m6oBAIECBAgQIAAnenm+gAAQABJREFUAQIECBAgQIAAAQIESiIgMC1JR2oGAQIECBAgQIAAAQIECBAgQIAAAQK1CwhMazdUAgECBAgQIECAAAECBAgQIECAAAECJREQmJakIzWDAAECBAgQIECAAAECBAgQIECAAIHaBQSmtRsqgQABAgQIECBAgAABAgQIECBAgACBkggITEvSkZpBgAABAgQIECBAgAABAgQIECBAgEDtAgLT2g2VQIAAAQIECBAgQIAAAQIECBAgQIBASQQEpiXpSM0gQIAAAQIECBAgQIAAAQIECBAgQKB2AYFp7YZKIECAAAECBAgQIECAAAECBAgQIECgJAK9StKOlma8+uqr6ZJLLklPPfVUeumll9JSSy2VVl555bT77runJZdcsuW46pWpU6emyy67LN17773p9ddfT8OGDUvrrLNO2m677VLPnj2rD21Zn59zWk5uZ2X8+PHp0ksvTc8++2zq27dvGjlyZBo9enRaZZVV2jn6v5u665wOK2AHAQIECBAgQIAAAQIECBAgQIAAgZIJ9Jj1wVKWNt1yyy3pmGOOSVOmTMlB5xJLLJEiQJ05c2bq06dP+u53v5u22mqr2Zr7xhtvpIMOOihNnDgxb1988cXTa6+9ltc322yzdNRRR6VFFlmk5nNmK6DVhwhrx4wZk7f269cvTZs2LX9FnY899tg0atSoVmekHPB2xzltLlyADUtffX0BaqmKBBpLYNJO23VJhdyPXcKq0JILuB9L3sGaVygB92OhuktlSy7QVfdjydk0jwCB+RQozSP5zz//fEtYus8++6S//OUv6fLLL8/f99577xyiRvhYCUYrXj/96U/ztg033DBdffXV6corr0wXXXRRWnXVVdNtt92WTj755MqhLd/n55yWk1utPPLII/kaEcoeffTR6dprr03XX399OuSQQ3KdDz/88DRp0qTZzuquc2a7qA8ECBAgQIAAAQIECBAgQIAAAQIEmkCgNIHpVVddlQPGrbfeOu27775p0UUXzd0X3/fff/+0xRZbpPfeey/FcZXlX//6V7r77rvz6NOf/exnaeDAgXnXsssum37xi1/kUarXXXddeuuttyqnpPk5p+XkdlbOPffcFIN899prrxQjWnv06JEWXnjhtNtuu6Vdd901TZ8+PV1xxRWzndld58x2UR8IECBAgAABAgQIECBAgAABAgQINIFAaQLTBx54IHfXpptu2m63xQjSWJ588smW/fEIfyybb7556t27d16v/BKP5m+wwQb50fgITSvL/JxTObf193fffTcHtrF92223bb27ZVuMfJ0xY0be313ntKmMDQQIECBAgAABAgQIECBAgAABAgSaQKA0gWk8On/xxRenjTbaqN1uq8xLOmjQoJb9jz76aF6vhKktO/63EoFpLA8//PD/tqQ0P+e0nNxqZdy4cXl06fLLL5+GDh3aam9Kw4cPT/3790+TJ09OEyZMyPu765w2lbGBAAECBAgQIECAAAECBAgQIECAQBMI9CpLG+PR+/ZCx2hfjM6sjBIdMWJES5Nj3tNYqkPUlp1V26vnPZ2fc6rLrF6fW1lxbNQtpgSIOqyyyiqpu86prue8rr/99tvp/fffn9fDHUeAQAMJvPnmmw1UG1Uh0NwC7sfm7n+tbywB92Nj9YfaNLdAI9yPvXr1Sosttlhzd4TWE2gSgdIEpnPqr9NPPz0999xzKeYm3XHHHVsOfeedd/J6R4HpgAED8v7KcfGhst6Zc3Ih7fwyt7LilNZ16K5z2qnuXDfFdAEzZ86c63EOIECg8QQqf7Y0Xs3UiEDzCbgfm6/PtbhxBdyPjds3atZ8Ao1wP8bLmgWmzfd7T4ubU6A0j+R31H3xxvv4WmihhdKRRx7ZMldpjISMl0DFEo+9t7f069cvb546dWr+Pj/ntFduZVsEjLF0dP3YV6lDpa7ddU5c20KAAAECBAgQIECAAAECBAgQIECg2QRKPcL0t7/9bfr973+fw9If/OAHaa211mrp3whQ+/Tpk6ZMmZIqgWjLzv+tVLbHT5FimZ9z/ldUu9/69u2bt0+bNq3d/bGxUoeYciCW7jonX6yTvwwcODDPydrJ0xxOgEADCAwePLgBaqEKBAiEgPvR7wMCjSPgfmycvlATAo1wP0YmYCFAoDkEShmYTp8+PR177LFp7NixKcLOH/7wh2nzzTdv06NDhgzJc4PGHKHtLZXtlZAyjpmfc9oru1JWfJ/TXCyt6xDX745z8kU6+Usl1O3kaQ4nQKABBHr37t0AtVAFAgRCwP3o9wGBxhFwPzZOX6gJAfej3wMECHSnQOl+PBIB47e+9a0clsb8n7/85S/bDUsDuRI+VkLJ1vCVILP6J1nzc07rciuf51ZWHNe6Dt11TqWOvhMgQIAAAQIECBAgQIAAAQIECBBoJoFSBaYRLh588MHpgQceSMstt1w67bTT0siRIzvsz6WWWirv+/e//93uMZXta6yxRsv++Tmn5eRWK5WyJk6cmGJUbOtl8uTJ6bXXXstTAQwbNizv7q5zWtfFZwIECBAgQIAAAQIECBAgQIAAAQLNIFCawHTWrFnpe9/7XnrqqafS6quvnsPS5Zdffo59uNVWW+X9N954Y5vj4gVPN910U96+zjrrtOyfn3NaTm61MnTo0DR8+PD09ttvp7vuuqvV3pRuvvnm/Nb5OKbyJr7uOqdNZWwgQIAAAQIECBAgQIAAAQIECBAg0AQCpQlMr7rqqvTII4/kx+xPOOGEFC8gmtuy0UYbpZVWWik98cQT6brrrpvt8PPPPz+9+uqracUVV0wbbrhhy775OSdOvu2229INN9yQJkyY0FJWrOyxxx7589lnn52qpwZ4+eWX04UXXpj37bbbbvl75ZfuOqdyPd8JECBAgAABAgQIECBAgAABAgQINItAKV76FG+Sj8fvY3nllVfSZz7zmQ77b7XVVktnnnlm3t+jR4+0//7755dCHXPMMenOO+9M8eh7BK+xvvDCC6fvfOc7KY6rLPNzTpwbIe4bb7yRDjnkkLTCCitUisvzq8Yj/+PGjUv77bdf2nLLLdOMGTNSjHqNwHbjjTdOo0ePbjk+VuIFVt1xzmwX9YEAAQIECBAgQIAAAQIECBAgQIBAEwj0+OBR9llFb+f48eNz8Dkv7Vh11VXTOeecM9uhMedpBKaTJk1q2R4jTw877LA0atSolm3VK50955Of/GRLYNp6xGgEvieddFJ+UVVlLtOePXvm4PfAAw9s92253XVOdZuLsr701dcXparqSaBhBCbttF2X1MX92CWsCi25gPux5B2seYUScD8WqrtUtuQCXXU/lpxN8wgQmE+BUgSm89n2NqfFiM54AVO8WGnppZfOL1tqc1CrDZ05Jx6ljwB0iy22aFXKfz/GyNKYgzUy7Jh/tW/fvu0eV72xu86pvmajrwtoGr2H1K8RBbrqH6Dux0bsbXVqdAH3Y6P3kPo1k4D7sZl6W1sbXaCr7sdGb7f6ESCwYARK8Uh+veiWWGKJFF+dWeb1nAhin3vuuTRixIgOi+/Vq1d+YVWHB7Szo7vOaefSNhEgQIAAAQIECBAgQIAAAQIECBAonUBpXvrU6D1z5JFHpn333TctueSSjV5V9SNAgAABAgQIECBAgAABAgQIECDQtAJGmHZT1//yl7/s9OjVbqqayxAgQIAAAQIECBAgQIAAAQIECBAg8D8BI0y76bdCZx/176ZquQwBAgQIECBAgAABAgQIECBAgAABAlUCAtMqDKsECBAgQIAAAQIECBAgQIAAAQIECDS3gMC0uftf6wkQIECAAAECBAgQIECAAAECBAgQqBIQmFZhWCVAgAABAgQIECBAgAABAgQIECBAoLkFBKbN3f9aT4AAAQIECBAgQIAAAQIECBAgQIBAlYDAtArDKgECBAgQIECAAAECBAgQIECAAAECzS0gMG3u/td6AgQIECBAgAABAgQIECBAgAABAgSqBASmVRhWCRAgQIAAAQIECBAgQIAAAQIECBBobgGBaXP3v9YTIECAAAECBAgQIECAAAECBAgQIFAlIDCtwrBKgAABAgQIECBAgAABAgQIECBAgEBzCwhMm7v/tZ4AAQIECBAgQIAAAQIECBAgQIAAgSoBgWkVhlUCBAgQIECAAAECBAgQIECAAAECBJpbQGDa3P2v9QQIECBAgAABAgQIECBAgAABAgQIVAkITKswrBIgQIAAAQIECBAgQIAAAQIECBAg0NwCAtPm7n+tJ0CAAAECBAgQIECAAAECBAgQIECgSkBgWoVhlQABAgQIECBAgAABAgQIECBAgACB5hYQmDZ3/2s9AQIECBAgQIAAAQIECBAgQIAAAQJVAgLTKgyrBAgQIECAAAECBAgQIECAAAECBAg0t4DAtLn7X+sJECBAgAABAgQIECBAgAABAgQIEKgSEJhWYVglQIAAAQIECBAgQIAAAQIECBAgQKC5BQSmzd3/Wk+AAAECBAgQIECAAAECBAgQIECAQJWAwLQKwyoBAgQIECBAgAABAgQIECBAgAABAs0tIDBt7v7XegIECBAgQIAAAQIECBAgQIAAAQIEqgQEplUYVgkQIECAAAECBAgQIECAAAECBAgQaG4BgWlz97/WEyBAgAABAgQIECBAgAABAgQIECBQJSAwrcKwSoAAAQIECBAgQIAAAQIECBAgQIBAcwsITJu7/7WeAAECBAgQIECAAAECBAgQIECAAIEqAYFpFYZVAgQIECBAgAABAgQIECBAgAABAgSaW0Bg2tz9r/UECBAgQIAAAQIECBAgQIAAAQIECFQJCEyrMKwSIECAAAECBAgQIECAAAECBAgQINDcAgLT5u5/rSdAgAABAgQIECBAgAABAgQIECBAoEpAYFqFYZUAAQIECBAgQIAAAQIECBAgQIAAgeYWEJg2d/9rPQECBAgQIECAAAECBAgQIECAAAECVQIC0yoMqwQIECBAgAABAgQIECBAgAABAgQINLeAwLS5+1/rCRAgQIAAAQIECBAgQIAAAQIECBCoEhCYVmFYJUCAAAECBAgQIECAAAECBAgQIECguQUEps3d/1pPgAABAgQIECBAgAABAgQIECBAgECVgMC0CsMqAQIECBAgQIAAAQIECBAgQIAAAQLNLSAwbe7+13oCBAgQIECAAAECBAgQIECAAAECBKoEBKZVGFYJECBAgAABAgQIECBAgAABAgQIEGhuAYFpc/e/1hMgQIAAAQIECBAgQIAAAQIECBAgUCUgMK3CsEqAAAECBAgQIECAAAECBAgQIECAQHMLCEybu/+1ngABAgQIECBAgAABAgQIECBAgACBKgGBaRWGVQIECBAgQIAAAQIECBAgQIAAAQIEmltAYNrc/a/1BAgQIECAAAECBAgQIECAAAECBAhUCQhMqzCsEiBAgAABAgQIECBAgAABAgQIECDQ3AIC0+buf60nQIAAAQIECBAgQIAAAQIECBAgQKBKQGBahWGVAAECBAgQIECAAAECBAgQIECAAIHmFhCYNnf/az0BAgQIECBAgAABAgQIECBAgAABAlUCAtMqDKsECBAgQIAAAQIECBAgQIAAAQIECDS3gMC0uftf6wkQIECAAAECBAgQIECAAAECBAgQqBIQmFZhWCVAgAABAgQIECBAgAABAgQIECBAoLkFBKbN3f9aT4AAAQIECBAgQIAAAQIECBAgQIBAlYDAtArDKgECBAgQIECAAAECBAgQIECAAAECzS0gMG3u/td6AgQIECBAgAABAgQIECBAgAABAgSqBASmVRhWCRAgQIAAAQIECBAgQIAAAQIECBBobgGBaXP3v9YTIECAAAECBAgQIECAAAECBAgQIFAlIDCtwrBKgAABAgQIECBAgAABAgQIECBAgEBzCwhMm7v/tZ4AAQIECBAgQIAAAQIECBAgQIAAgSoBgWkVhlUCBAgQIECAAAECBAgQIECAAAECBJpbQGDa3P2v9QQIECBAgAABAgQIECBAgAABAgQIVAkITKswrBIgQIAAAQIECBAgQIAAAQIECBAg0NwCAtPm7n+tJ0CAAAECBAgQIECAAAECBAgQIECgSkBgWoVhlQABAgQIECBAgAABAgQIECBAgACB5hYQmDZ3/2s9AQIECBAgQIAAAQIECBAgQIAAAQJVAgLTKgyrBAgQIECAAAECBAgQIECAAAECBAg0t4DAtLn7X+sJECBAgAABAgQIECBAgAABAgQIEKgSEJhWYVglQIAAAQIECBAgQIAAAQIECBAgQKC5BQSmzd3/Wk+AAAECBAgQIECAAAECBAgQIECAQJWAwLQKwyoBAgQIECBAgAABAgQIECBAgAABAs0tIDBt7v7XegIECBAgQIAAAQIECBAgQIAAAQIEqgQEplUYVgkQIECAAAECBAgQIECAAAECBAgQaG6BXs3dfK2vp8DLL7+cZs6cWc8ilUWAQDcJvPjii910JZchQGBuAu7HuQnZT6D7BNyP3WftSgTmJtAI9+MiiyySllhiiblV1X4CBEogIDAtQSc2ShP69euXZs2a1SjVUQ8CBDohMGDAgE4c7VACBLpSwP3YlbrKJtA5Afdj57wcTaArBRrhfuzZs2dXNlHZBAg0kIDAtIE6o+hVWWyxxYreBPUn0LQCffv2bdq2aziBRhNwPzZaj6hPMwu4H5u597W90QTcj43WI+pDoNwC5jAtd/9qHQECBAgQIECAAAECBAgQIECAAAECnRAQmHYCy6EECBAgQIAAAQIECBAgQIAAAQIECJRbwCP55e5frSNAgAABAgQIECBAoMEEBk6b2mA1Uh0CBAgQIECgWkBgWq1hnQABAgQIECBAgAABAl0scN+Vl3TxFRRPoIQCu3y6hI3SJAIEGlXAI/mN2jPqRYAAAQIECBAgQIAAAQIECBAgQIBAtwsITLud3AUJECBAgAABAgQIECBAgAABAgQIEGhUAYFpo/aMehEgQIAAAQIECBAgQIAAAQIECBAg0O0CAtNuJ3dBAgQIECBAgAABAgQIECBAgAABAgQaVUBg2qg9o14ECBAgQIAAAQIECBAgQIAAAQIECHS7gMC028ldkAABAgQIECBAgAABAgQIECBAgACBRhUQmDZqz6gXAQIECBAgQIAAAQIECBAgQIAAAQLdLiAw7XZyFyRAgAABAgQIECBAgAABAgQIECBAoFEFejVqxdSLAAECBAgQIECgfgIDp02tX2FKIkCAAAECBAgQIFBiAYFpiTtX0wgQIECAAAECFYH7rryksuo7AQLzKrDLp+f1SMcRIECAAAECJRLwSH6JOlNTCBAgQIAAAQIECBAgQIAAAQIECBCoTUBgWpufswkQIECAAAECBAgQIECAAAECBAgQKJGAwLREnakpBAgQIECAAAECBAgQIECAAAECBAjUJiAwrc3P2QQIECBAgAABAgQIECBAgAABAgQIlEhAYFqiztQUAgQIECBAgAABAgQIECBAgAABAgRqExCY1ubnbAIECBAgQIAAAQIECBAgQIAAAQIESiQgMC1RZ2oKAQIECBAgQIAAAQIECBAgQIAAAQK1CQhMa/NzNgECBAgQIECAAAECBAgQIECAAAECJRIQmJaoMzWFAAECBAgQIECAAAECBAgQIECAAIHaBASmtfk5mwABAgQIECBAgAABAgQIECBAgACBEgkITEvUmZpCgAABAgQIECBAgAABAgQIECBAgEBtAgLT2vycTYAAAQIECBAgQIAAAQIECBAgQIBAiQQEpiXqTE0hQIAAAQIECBAgQIAAAQIECBAgQKA2AYFpbX7OJkCAAAECBAgQIECAAAECBAgQIECgRAIC0xJ1pqYQIECAAAECBAgQIECAAAECBAgQIFCbgMC0Nj9nEyBAgAABAgQIECBAgAABAgQIECBQIoFeJWqLphAgQIBAgwkMnDa1wWqkOgQIECBAgAABAgQIECBAYM4CAtM5+9hLgAABAjUI3HflJTWc7VQCTSqwy6ebtOGaTYAAAQIECBAgQKAxBDyS3xj9oBYECBAgQIAAAQIECBAgQIAAAQIECDSAgMC0ATpBFQgQIECAAAECBAgQIECAAAECBAgQaAwBgWlj9INaECBAgAABAgQIECBAgAABAgQIECDQAAIC0wboBFUgQIAAAQIECBAgQIAAAQIECBAgQKAxBASmjdEPakGAAAECBAgQIECAAAECBAgQIECAQAMICEwboBNUgQABAgQIECBAgAABAgQIECBAgACBxhAQmDZGP6gFAQIECBAgQIAAAQIECBAgQIAAAQINICAwbYBOUAUCBAgQIECAAAECBAgQIECAAAECBBpDoFdjVEMtCNRPYOC0qfUrTEkECBAgQIAAAQIECBAgQIAAAQJNJSAwbarubo7G3nflJc3RUK0kUE+BXT5dz9KURYAAAQIECBAgQIAAAQIECivgkfzCdp2KEyBAgAABAgQIECBAgAABAgQIECBQbwGBab1FlUeAAAECBAgQIECAAAECBAgQIECAQGEFBKaF7ToVJ0CAAAECBAgQIECAAAECBAgQIECg3gIC03qLKo8AAQIECBAgQIAAAQIECBAgQIAAgcIKCEwL23UqToAAAQIECBAgQIAAAQIECBAgQIBAvQUEpvUWVR4BAgQIECBAgAABAgQIECBAgAABAoUVEJgWtutUnAABAgQIECBAgAABAgQIECBAgACBegsITOstqjwCBAgQIECAAAECBAgQIECAAAECBAorIDAtbNepOAECBAgQIECAAAECBAgQIECAAAEC9RYQmNZbVHkECBAgQIAAAQIECBAgQIAAAQIECBRWQGBa2K5TcQIECBAgQIAAAQIECBAgQIAAAQIE6i0gMK23qPIIECBAgAABAgQIECBAgAABAgQIECisgMC0sF2n4gQIECBAgAABAgQIECBAgAABAgQI1FtAYFpvUeURIECAAAECBAgQIECAAAECBAgQIFBYAYFpYbtOxQkQIECAAAECBAgQIECAAAECBAgQqLeAwLTeosojQIAAAQIECBAgQIAAAQIECBAgQKCwAgLTwnadihMgQIAAAQIECBAgQIAAAQIECBAgUG8BgWm9RZVHgAABAgQIECBAgAABAgQIECBAgEBhBQSmhe06FSdAgAABAgQIECBAgAABAgQIECBAoN4CAtN6iyqPAAECBAgQIECAAAECBAgQIECAAIHCCghMC9t1Kk6AAAECBAgQIECAAAECBAgQIECAQL0FBKb1FlUeAQIECBAgQIAAAQIECBAgQIAAAQKFFRCYFrbrVJwAAQIECBAgQIAAAQIECBAgQIAAgXoLCEzrLao8AgQIECBAgAABAgQIECBAgAABAgQKKyAwLWzXqTgBAgQIECBAgAABAgQIECBAgAABAvUWEJjWW1R5BAgQIECAAAECBAgQIECAAAECBAgUVkBgWtiuU3ECBAgQIECAAAECBAgQIECAAAECBOotIDCtt6jyCBAgQIAAAQIECBAgQIAAAQIECBAorIDAtLBdp+IECBAgQIAAAQIECBAgQIAAAQIECNRbQGBab1HlESBAgAABAgQIECBAgAABAgQIECBQWAGBaWG7TsUJECBAgAABAgQIECBAgAABAgQIEKi3gMC03qLKI0CAAAECBAgQIECAAAECBAgQIECgsAIC08J2nYoTIECAAAECBAgQIECAAAECBAgQIFBvgV71LlB5jS0wfvz4dOmll6Znn3029e3bN40cOTKNHj06rbLKKo1dcbUjQIAAAQIECBAgQIAAAQIECBAg0A0CAtNuQG6US1x22WVpzJgxuTr9+vVL06ZNS/fff3+65JJL0rHHHptGjRrVKFVVDwIECBAgQIAAAQIECBAgQIAAAQILRMAj+QuEvfsv+sgjj6STTz45LbLIIunoo49O1157bbr++uvTIYcckqZMmZIOP/zwNGnSpO6vmCsSIECAAAECBAgQIECAAAECBAgQaCABgWkDdUZXVuXcc89Ns2bNSnvttVfabLPNUo8ePdLCCy+cdtttt7Trrrum6dOnpyuuuKIrq6BsAgQIECBAgAABAgQIECBAgAABAg0vIDBt+C6qvYLvvvtuuvvuu3NB2267bZsCK9uuvvrqNGPGjDb7bSBAgAABAgQIECBAgAABAgQIECDQLAIC0ybo6XHjxuXRpcsvv3waOnRomxYPHz489e/fP02ePDlNmDChzX4bCBAgQIAAAQIECBAgQIAAAQIECDSLgMC0CXr6+eefz60cNGhQh62t7Js4cWKHx9hBgAABAgQIECBAgAABAgQIECBAoOwCvcreQO1L6Z133skMlVC0PZMBAwbkzZVj2ztmbtteffXVNHPmzLkd1uX7F+vyK7gAgfIJvPzyy13SKPdjl7AqtOQC7seSd7DmFUrA/Vio7lLZkgt01f3YGbZ4ifKc/l/dmbIcS4BAYwsITBu7f+pSu5jDNJZ47L6jpV+/fnnXe++919Ehc90eYWkjBKaTP/hLzEKAQOcEFuqiH3a4HzvXD44mEALuR78PCDSOgPuxcfpCTQh01f3YGdlG+P9uZ+rrWAIE5l9AYDr/doU5s2/fvrmu06ZN67DOU6dOzfsWXXTRDo8pyo6FfnJMUaqqngRKL+B+LH0Xa2CBBNyPBeosVS29gPux9F2sgQQIECBQcAGBacE7cF6qP2TIkHzYm2++2eHhb731Vt5XCVc7PHAOO5Zaaqk57LWrmQXi915luofBgwen3r17NzOHthNYoALx5/3bb7+d6zBw4MC02GImTligHeLiTS0QfzdW/n0W0yPV8u+wpobUeAJ1EIin8uIluLHEk3mVJ/DqULQiCBAgQKCAAl76VMBO62yVK4FpJRRt7/zKP9YjzLIQIECAAAECBAgQIECAAAECBAgQaFYBgWkT9Hxl5OfEiRPT9OnT27Q4fpL62muvpYUWWigNGzaszX4bCBAgQIAAAQIECBAgQIAAAQIECDSLgMC0CXp66NChafjw4fkRzLvuuqtNi2+++eb8sqY4xqOZbXhsIECAAAECBAgQIECAAAECBAgQaCIBgWmTdPYee+yRW3r22Wen6kfzX3755XThhRfmfbvttluTaGgmAQIECBAgQIAAAQIECBAgQIAAgfYFvPSpfZfSbd18883TGmuskcaNG5f222+/tOWWW6YZM2akG2+8Mb366qtp4403TqNHjy5duzWIAAECBAgQIECAAAECBAgQIECAQGcEBKad0SrwsT179kynnHJKOumkk9LYsWPT+eefn1sT23fdddd04IEH5jlMC9xEVSdAgAABAgQIECBAgAABAgQIECBQs0CPWR8sNZeigEIJxMjSp556KkXXL7/88qlv376Fqr/KFk/gzTffTO+8806u+ODBg1Pv3r2L1wg1JlASgZiW5e23386tGThwoLmrS9KvmlFMgfi7Mf6OjGXAgAH+TVbMblTrkgi8++67KV6GG0v//v1Tv379StIyzSBAgACB+REwwnR+1Ap+Tq9evdLqq69e8FaoPgECBAgQIECAAAECBAgQIECAAIH6C3jpU/1NlUiAAAECBAgQIECAAAECBAgQIECAQEEFBKYF7TjVJkCAAAECBAgQIECAAAECBAgQIECg/gIC0/qbKpEAAQIECBAgQIAAAQIECBAgQIAAgYIKCEwL2nGqTYAAAQIECBAgQIAAAQIECBAgQIBA/QUEpvU3VSIBAgQIECBAgAABAgQIECBAgAABAgUVEJgWtONUmwABAgQIECBAgAABAgQIECBAgACB+gsITOtvqkQCBAgQIECAAAECBAgQIECAAAECBAoqIDAtaMepNgECBAgQIECAAAECBAgQIECAAAEC9RcQmNbfVIkECBAgQIAAAQIECBAgQIAAAQIECBRUQGBa0I5TbQIECBAgQIAAAQIECBAgQIAAAQIE6i8gMK2/qRIJECBAgAABAgQIECBAgAABAgQIECiogMC0oB2n2gQIECBAgAABAgQIECBAgAABAgQI1F9AYFp/UyUSIECAAAECBAgQIECAAAECBAgQIFBQAYFpQTtOtQkQIECAAAECBAgQIECAAAECBAgQqL+AwLT+pkokQIAAAQIECBAgQIAAAQIECBAgQKCgAgLTgnacahMgQIAAAQIECBAgQIAAAQIECBAgUH8BgWn9TZVIgAABAgQIECBAgAABAgQIECBAgEBBBQSmBe041SZAgAABAgQIECBAgAABAgQIECBAoP4CAtP6myqRAAECBAgQIECAAAECBAgQIECAAIGCCghMC9pxqk2AAAECBAgQIECAAAECBAgQIECAQP0FBKb1N1UiAQIECBAgQIAAAQIECBAgQIAAAQIFFegx64OloHVXbQIECBAgQIAAAQIECBAgQIAAAQIECNRVwAjTunIqjAABAgQIECBAgAABAgQIECBAgACBIgsITIvce+pOgAABAgQIECBAgAABAgQIECBAgEBdBQSmdeVUGAECBAgQIECAAAECBAgQIECAAAECRRYQmBa599SdAAECBAgQIECAAAECBAgQIECAAIG6CghM68qpMAIECBAgQIAAAQIECBAgQIAAAQIEiiwgMC1y76k7AQIECBAgQIAAAQIECBAgQIAAAQJ1FRCY1pVTYQQIECBAgAABAgQIECBAgAABAgQIFFlAYFrk3lN3AgQIECBAgAABAgQIECBAgAABAgTqKiAwrSunwggQIECAAAECBAgQIECAAAECBAgQKLKAwLTIvafuBAgQIECAAAECBAgQIECAAAECBAjUVUBgWldOhREgQIAAAQIECBAgQIAAAQIECBAgUGQBgWmRe0/dCRAgQIAAAQIECBAgQIAAAQIECBCoq0CvupamMAIECi9w8803p9tvvz0999xz6f33308rrLBC+tjHPpY+8YlPdNi28ePHp0svvTQ9++yzqW/fvmnkyJFp9OjRaZVVVunwnOod11xzTTrvvPPSj370o7TGGmtU7+pwfdasWeknP/lJmjhxYjr66KPThz70oQ6PtYNAUQUa9X6Mej388MMdsg4ZMiTtueeeHe63g0ARBRr1fqxY3nHHHemuu+5Kjz32WOrRo0f+O/jzn/98/nu8cozvBMoisCDux3fffTcdfvjhaemll04//OEP21CeccYZ6a233mqzvfWGhRZaKB166KGtN/tMgAABAg0m0OOD0GFWg9VJdQgQWAACU6dOTd/+9rfTAw88kK8+YMCA/P3NN9/M39dZZ510/PHHpz59+sxWu8suuyyNGTMmb+vXr1+aNm1a/orjjj322DRq1KjZjm/94ZFHHkmHHHJImjFjRjrllFNSXGdelosuuij9+te/zodecMEFafnll5+X0xxDoBACjX4/xn/07rvvvg4t44cl5557bof77SBQJIFGvx+nT5+e//7805/+lFkHDhyY3nvvvRT17tmzZ/rBD36Qtt566yKRqyuBDgUW1P0Y/2U+4ogjUvxgYt11100nn3xymzrusssu6T//+U+b7a03RGB66623tt7sMwECBAg0mIARpg3WIapDYEEJRPgYYelKK62U/3O1+uqr56rE6NEYyfnggw/m/5B95zvfaalihJ3xD8ZFFlkkHXXUUWnTTTfNwecVV1yRt8dP4SPMjJ/Et7fE9eK8CEs7szz55JPp9NNP78wpjiVQKIFGvx+feOKJ7Bk/7Fh00UXb2Pbv37/NNhsIFFWg0e/Hs846K0VYuuSSS+a/U+Mpjwh3/vCHP6QzzzwzHXfccfnJD09iFPV3oHpXCyyI+3HKlCl5cECEpXNavvrVr+YfVrR3zMyZM/P9GAMRdtxxx/YOsY0AAQIEGkzAHKYN1iGqQ2BBCMQjRn/+859T/MQ7wtFKWBp1GT58eH7kPdavvvrqFMdWlhhBFv8p22uvvdJmm22WHwFceOGF02677ZZ23XXXFKNeIjxtvUQZP//5z/PI0tdffz1ft/UxHX2OkQVRx169euWgtqPjbCdQVIFGvx9ffvnlFP/hW2KJJfK9/qlPfSq1/tpyyy2Lyq/eBGYTaPT78Z133slhafz9HdParL322vnv1BhZ+qUvfSkNGzYsBzhGs83WrT4UVKC778dguvfee9Pee++dYvqouM/mtGyzzTZt/j6s/P0Y/96NvztHjBiRDjvssDkVYx8BAgQINIjAnP/Ub5BKqgYBAl0rECNF4yff8Vj7yiuv3OZisS1GrkQ4+u9//zvvj3+03n333Xl92223bXNOZVuErK1HkO63337pyiuvTIsttlieA6q9a7Yp8H8bTjvttPT000+ngw8+OJ/f0XG2EyiqQKPfj5XRpdU/WCmqtXoTmJtAo9+P8UPJCE3jh5RrrbVWm+Z885vfTN/61rdySNNmpw0ECibQ3ffjDTfckMPNSZMmpQ033DDF/TQ/Szyldc455+QnMuLJqhhcYCFAgACBxhfwSH7j95EaEuhygQ022CCPMI05z9pbIvCcPHly3jVo0KD8fdy4cTlAjZB16NChbU6LkanxWG6cN2HChNleAPXGG2+kCFS/8pWvpGWWWSadf/75bc5vb8M999yTYs7UeAlV/MQ+Jte3ECibQKPfj60D0/jzIUbNLL744mXrCu0hkBr9fozRb7Fssskm7fbWmmuumeLLQqAMAt19P7722mv537gxwnSHHXbIL0XtrGP8HXniiSfmfzPvs88++d+9nS3D8QQIECCwYAQEpgvG3VUJNJRAvE138ODBHdZp7Nix+UVO8SKJZZddNh/3/PPP5++VALW9k2NfvC003mQfL4GpLGeffXan32ofwevRRx+dog7f+973KkX5TqB0Ao1+P1YC0/hPYDxWGHMRxwj1+AHJ+uuvn6faiMf1LQTKINDo9+Mrr7ySmVdbbbX07LPPpuuuuy499NBDeUqcGAW+++67pxVWWKEMXaENBPLUT93579Wtttoqffazn83TQM0v/+WXX56eeeaZ/BRX3I8WAgQIECiOgMC0OH2lpgQWiMALL7yQTj311HztAw44IP9jNT7EI4CxzCkwHTBgQD6mcmz+8MEv8/PiiRNOOCG9+uqrOTQ1kq0i6XuzCTTC/RgvXYvl97//fX4Dd4wmj/A0puu46aab8nxvY8aMSRHgWAiUWaAR7seYUzjmVYypauKHifFDynjcN+YQf+yxx1L8wDO2R/BjIVBmga64H4cMGVITWfww8eKLL85lxPz+Mf++hQABAgSKI2AO0+L0lZoS6HaBCChjvqZ4hD4eg4rH4CtLzGEay5zeht2vX798TEeP+ued8/BLTLQfL6zYfvvt88ul5uEUhxAonUAj3I/xw4/4T2ksMa1GjGaLeYXjTdzxRu6PfOQj+fH8Y445ps3cxaXrEA1qaoFGuB/j7+H4ilGw8Xf1hz/84fyDjL/+9a/5RVDxCHH8/Xvssceml156qan7S+PLLdAI92N7wnfddVf6z3/+k+fcr8zt395xthEgQIBAYwoITBuzX9SKwAIXiHlHv/a1r6V49D5CkHgzffXSt2/f/HHatGnVm2dbjzfax7LooovOtr0zHyKcidFqSy+9dPrGN77RmVMdS6A0Ao1yP/bp0yddcMEF6eSTT07f//73U3yuLDGXcfw5Efd7PLZfeSlcZb/vBMoi0Cj3Y4wijSVGscWTG/EkxkorrZQD1BgZd8QRR6T11lsvh6bnnntuWfi1g8BsAo1yP85Wqf99uOqqq/Ladttt50Wl7QHZRoAAgQYXEJg2eAepHoEFIfDwww+nr371q+nFF1/M/9n6xS9+kSoBaaU+lceU4mUvHS3xaGAsrc/t6PjW2+M/gRHAxAiZCGfmt5zW5fpMoEgCjXI/hlk8+hsvelt33XVbpueotozQZuTIkXlTPKJvIVA2gUa6H2NO70UWWSQT77zzzu2+eXuXXXbJ+x9//PGydYX2EEiNdD+27o6YX/jOO+/Mmz/zmc+03u0zAQIECBRAwEQqBegkVSTQnQIxB+HPfvazPP9ZPD4Uc5+1N+dSJTCthKLt1bESps5pgv72zqtsi7kSH3300RzS/N///V9lc8v3mCoglgh3I8iJ7zvuuGPLfisEii7QSPfjvFoutdRS+dB4RNJCoEwCjXg/xt/F8SRGjPBub6m8qDF+AGohUCaBRrwfq32vvfbaPPp71KhReeR39T7rBAgQIFAMAYFpMfpJLQl0i0A8OnT88cfna+2zzz5p33337fC6lVBk4sSJOVyNl0xUL/FW+9deey0HmcOGDave1an1nj175uPnFMxWXipVeTyxUxdwMIEGFWjE+zFeIvOPf/wjP1oYL7Bob4mX0MSy3HLLtbfbNgKFFGjE+zEg4+/iCEzjLdwbb7xxG9vKDxZXWWWVNvtsIFBUgUa9H6s9I9CNJebftxAgQIBAMQUEpsXsN7UmUHeBCEFi/rN4ecR3v/vduY7UjNEs8Xbs8ePHp5jUfpNNNpmtTjfffHP+yXrMf7rYYovNtm9eP6y++urplltu6fDwT37yk/mFVPG27nhM2EKgLAKNeD+G7euvv55f8BR/TsSL4FZcccXZyOOHJDEqPJYRI0bMts8HAkUVaNT7MTy32mqr9OCDD6Z77rkn7bnnnm2IH3jggbytMlVGmwNsIFAwgUa+HyuU8TK2yrQ0tQwaqJTnOwECBAgsGAFzmC4Yd1cl0FAC8XKmk046Kc2aNSvtt99+cw1LK5XfY4898urZZ5+dqkeAxgizCy+8MO/raBRapQzfCRCYXaCR78d11lknDRo0KP9Zcc4556QZM2a0VD7mGj7uuOPSlClT8ki3+IGKhUDRBRr5fgzbHXbYIS2xxBLpvvvuS+edd95s3E899VS66KKLUjyp0d7o09kO9oFAAQQa/X6sEMZggvg3ddx7K6ywQmWz7wQIECBQMAEjTAvWYapLoCsELrvssvxIX5R91lln5a+OrhPzm1ZGk26++eZpjTXWSOPGjctB65ZbbpkDlBtvvDHF/IXxH7TRo0d3VJTtBAi0I9DI92Pv3r3TUUcdlb75zW+muM8jpNlmm23yfwpvu+229Nxzz6WVV145HXbYYe20zCYCxRNo5PsxNOOlT3E//vjHP05nnHFGfuJj/fXXT/HCmeuvvz5FwBRzkRvxXbzfe2rcVqDR78dKjZ9//vm8GlPTtJ6yqnKM7wQIECDQ+AIC08bvIzUk0OUCDz30UMs14s30c1ref//9lt3xk/NTTjklj04dO3ZsOv/88/O+2L7rrrumAw88MM9h2nKCFQIE5irQ6Pfjeuutl0499dQ0ZsyY/MOSiy++OLepT58+KV4UF+HN/E7DMVccBxDoZoFGvx+DY7PNNkunn356OvbYY9MjjzyS3xwe21dbbbW08847z/NTI3GOhUAjCxThfgy/yksPzR3cyL+b1I0AAQJzF+jxweMCs+Z+mCMIECAwZ4F4NDce/4s/UmI+0b59+875BHsJEOgyge66H+PlbvHit/79++f7fqGFzPTTZZ2q4MIKdNf9GECVuROXXXbZNHjw4MKaqTiBrhLozvuxq9qgXAIECBDoHgGBafc4uwoBAgQIECBAgAABAgQIECBAgAABAgUQMBSkAJ2kigQIECBAgAABAgQIECBAgAABAgQIdI+AwLR7nF2FAAECBAgQIECAAAECBAgQIECAAIECCAhMC9BJqkiAAAECBAgQIECAAAECBAgQIECAQPcICEy7x9lVCBAgQIAAAQIECBAgQIAAAQIECBAogIDAtACdpIoECBAgQIAAAQIECBAgQIAAAQIECHSPgMC0e5xdhQABAgQIECBAgAABAgQIECBAgACBAggITAvQSapIgAABAgQIECBAgAABAgQIECBAgED3CAhMu8fZVQgQIECAAAECBAgQIECAAAECBAgQKICAwLQAnaSKBAgQIECAAAECBAgQIECAAAECBAh0j4DAtHucXYUAAQIECBAgQIAAAQIECBAgQIAAgQIICEwL0EmqSIAAAQIECBAgQIAAAQIECBAgQIBA9wgITLvH2VUIECBAgACBEgo888wzqX///qlHjx7569prr+1UK7fddtuWc3/605926lwHEyBAgAABAgQIECDQNQIC065xVSoBAgQIECDQBAIrrbRSOv7441taeuCBB6Y333yz5fOcVn73u9+lsWPH5kM23HDDdOSRR87pcPsIECBAgAABAgQIEOgmgR6zPli66VouQ4AAAQIECBAonUD8U2rrrbdON910U27bAQcckE4//fQ5tvP5559PI0aMSJMnT06LLbZYevDBB9OwYcPmeI6dBAgQIECAAAECBAh0j4ARpt3j7CoECBAgQIBASQXicfwYLdqvX7/cwjPOOCPdfPPNc2xtjESNsDSWE044QVg6Ry07CRAgQIAAAQIECHSvgMC0e71djQABAgQIECihQDyaH8FnLDHidL/99kvvvvtuuy39/e9/n6655pq8b7vttksHHXRQu8fZSIAAAQIECBAgQIDAghHwSP6CcXdVAgQIECBAoGQCEZR+4hOfSH/9619zyw499NB00kknzdbKSZMm5UfxX3vttbT44ounRx55JA0dOnS2Y1p/iHKfeOKJ9NBDD6VHH300H7/OOuukkSNHpj59+rQ+vM3nKVOmpMcffzyNHz8+PfbYY6l37955RGtMAfCRj3wkLbRQ25+fT58+PT3wwAO5rDgmpg34+9//nqcdWGaZZdIuu+ySllhiiTbXsoEAAQIECBAgQIBAGQQEpmXoRW0gQIAAAQIEGkLg2WefzUHmW2+9lYPIO++8M22wwQYtddttt93SZZddlj9ffPHF6XOf+1zLvvZWnnzyybTXXnulu+66q83upZZaKk8FsNNOO7XZV9kQc6kefvjh6e23365smu17vGzqzDPPTGuuueZs2yPYjWA0ljvuuCOdcsop6aKLLmo5ZsCAAemFF15Iffv2bdlmhQABAgQIECBAgEBZBASmZelJ7SBAgAABAgQaQuC3v/1tijlKY1lvvfVy2BmjOK+77rq0ww475O1f+MIX0vnnn5/XO/rlj3/8Y9p7773TO++8k2Ke1PXXXz+PTp04cWK6++6705tvvplP/c53vpOOO+642YqJUak77rhjvmbsiFGsG220UR7VGkFnnP/KK6/kc2Lu1QkTJqTBgwe3lFEdmH75y19O55xzTsu+WNl+++3TtddeO9s2HwgQIECAAAECBAiURUBgWpae1A4CBAgQIECgYQS22WabdMMNN+T6nHrqqelLX/pSDjuffvrptNxyy+VH8QcNGtRhfV9//fX82Pyrr76all9++RTznm6++eYtx8f2PfbYo+Uaf/vb39LGG2/csj/CzAhMY/n617+epwZYeOGFW/bHiNODDz64JQg95phj0hFHHNGyvzowjY0x2vSHP/xhDl1vvfXWPIp29OjRLcdbIUCAAAECBAgQIFAmAYFpmXpTWwgQIECAAIGGEIgRm/GYezyaH3OVxqP3p512Wh4pOnbs2LT11lvPsZ4RZv7qV7/Kj/Xffvvt6eMf/3ib42fMmJGvEfOSjho1Kt1zzz0t85Fuu+22Ka4T4ewzzzyTevbs2eb8GLkaoW2U8+lPfzpdccUVLce0Dkz/8pe/pAiBLQQIECBAgAABAgSaQaDtLP/N0GptJECAAAECBAh0ocAKK6yQTjzxxHyFeMFThKWxRBA6t7B05syZ6YwzzsjHx6Pv7YWlsbNXr17pkEMOycfdf//96Z///Gdej1+OPfbYdMkll6Szzjqr3bA0jon5RyNQjaWjOU5j3+qrry4sDQgLAQIECBAgQIBA0wj0apqWaigBAgQIECBAoBsF9t9///yCpxjpGcvw4cNzkDm3KsTo1KlTp+bD1l577RSP53e0fPjDH27Z9cQTT6S11lorf1533XVTfLW3TJ48OT388MPplltuSfFofywxyrSjZdiwYR3tsp0AAQIECBAgQIBAKQUEpqXsVo0iQIAAAQIEGkHggAMOyI/GR1323Xff1KdPn7lW6/HHH285JuYWja95WSIwbb1EIBojTR955JH06KOPpnh8/6WXXmp92Bw/r7baanPcbycBAgQIECBAgACBsgkITMvWo9pDgAABAgQINIxAvN2+slSvV7a19/25555rb/Nct02cOHG2Y4488sg0ZsyY9O677862PT7EqNF4KdQFF1yQXn755Tb7qzcMGTKk+qN1AgQIECBAgAABAqUXEJiWvos1kAABAgQIECiSQGVe0ajzeeedl3beeed5qv4iiyzScly88T7mMY0lXjoVL3Vab7318iP7I0eOTAMHDsz7ql/0lDf4hQABAgQIECBAgACBJDD1m4AAAQIECBAg0EAC1XOGxiP0/fv371Tt4jH84447Lp+zxhprpBtuuCEtu+yy7ZYRL6SKJV40ZSFAgAABAgQIECBA4L8CC4EgQIAAAQIECBBoHIGVVlop9e7dO1fommuuSe+//36Hlbv00ktTvBgqRpD+7W9/y8fddtttadasWXn9wAMP7DAsHTduXHrzzTfzcXN66VOHF7eDAAECBAgQIECAQEkFBKYl7VjNIkCAAAECBIopsNBCC6VvfOMbufIPPvhgOvXUU9ttyNtvv51++MMf5jfejx07Ng0fPjwfN23atJbjn3rqqZb16pUISvfee++WTdOnT29Zt0KAAAECBAgQIECg2QUEps3+O0D7CRAgQIAAgYYT+MEPftAyMjTC0+9///vpnXfeyfWcOnVquu6669IOO+yQxo8fn7cddNBBqfJypvXXXz9VXjB11llnpVtvvbXlkfsYrXr//ffnc++9996Wdr/++ust61YIECBAgAABAgQINLuAwLTZfwdoPwECBAgQINBwAv369UsXXXRRWn755XPYecwxx6QBAwbkUaQRjEZYevvtt+d677LLLun4449vacMqq6ySDj/88Pw5QtYtttgifehDH0qjR4/OL4D66Ec/mu644460++67p3322Scf99xzz6U33nijpQwrBAgQIECAAAECBJpZQGDazL2v7QQIECBAgEDDCmyyySbpn//8Z9p///1zWBqjQ+MlUPEofiwrr7xyOuOMM9LFF1+cevbsOVs7ImD9+c9/ngYNGpS3x4ugbr755jxn6ZprrplHqEYgu+eee+b98Rj/5ZdfPlsZPhAgQIAAAQIECBBoVoEeH7wU4L9vBWhWAe0mQIAAAQIECBRAYOLEienRRx/NL4SKUaTLLbdcivlO57S89957KeYxffrpp9NSSy2VRowYkfr27TunU+wjQIAAAQIECBAg0PQCAtOm/y0AgAABAgQIECBAgAABAgQIECBAgACBisCchyVUjvKdAAECBAgQIECAAAECBAgQIECAAAECTSAgMG2CTtZEAgQIECBAgAABAgQIECBAgAABAgTmTUBgOm9OjiJAgAABAgQIECBAgAABAgQIECBAoAkEBKZN0MmaSIAAAQIECBAgQIAAAQIECBAgQIDAvAkITOfNyVEECBAgQIAAAQIECBAgQIAAAQIECDSBgMC0CTpZEwkQIECAAAECBAgQIECAAAECBAgQmDcBgem8OTmKAAECBAgQIECAAAECBAgQIECAAIEmEBCYNkEnayIBAgQIECBAgAABAgQIECBAgAABAvMmIDCdNydHESBAgAABAgQIECBAgAABAgQIECDQBAIC0yboZE0kQIAAAQIECBAgQIAAAQIECBAgQGDeBASm8+bkKAIECBAgQIAAAQIECBAgQIAAAQIEmkBAYNoEnayJBAgQIECAAAECBAgQIECAAAECBAjMm4DAdN6cHEWAAAECBAgQIECAAAECBAgQIECAQBMICEyboJM1kQABAgQIECBAgAABAgQIECBAgACBeRMQmM6bk6MIECBAgAABAgQIECBAgAABAgQIEGgCAYFpE3SyJv5/O3ZIAAAAgDCsf2sq3DOPYTgIECBAgAABAnIFrQ8AAABJSURBVAQIECBAgAABAgQIEGgCDtPmJEWAAAECBAgQIECAAAECBAgQIECAwIGAw/RgZBUJECBAgAABAgQIECBAgAABAgQIEGgCA5vin9wtLZbfAAAAAElFTkSuQmCC" />

Sales and Profit Analysis

1. Overall Trend

• Sales and Profit are steadily growing (except for 2015, when sales slightly declined).
  
• In 2015, sales decreased, but profit increased, indicating a change in pricing strategy or cost reduction.

2. Seasonal Fluctuations

High and Low Sales Periods

• The highest sales occur from September to December.
  
• The lowest sales are observed in January and February.

Possible Reasons for Seasonal Fluctuations

• High sales at the end of the year may be linked to the holiday shopping season (Black Friday, Christmas, New Year).
  
• The decline at the beginning of the year is due to lower consumer spending after high expenses in December.

3. Next Steps

• A detailed analysis of sales dynamics by product category could provide better insights into the reasons for seasonal fluctuations.

===============================================================

Question 2: Which product categories have the best/worst profit margins?

Objective: Identify high/low performing product categories

Tasks:

• Identify which category has the thinnest margins

• Calculate the profit margin difference between top and bottom categories

• Suggest strategies to improve low-performing categories

superstore <- superstore %>%
  mutate(Profit.Margin = percent(Profit / Sales))

category_sales <- superstore %>%
  group_by(Category, Sub.Category) %>%
  summarise(
    Total_Sales = sum(Sales),
    Total_Profit = sum(Profit),
    Profit_Margin = Total_Profit / Total_Sales,
    Mean.Discount =  1 - round(sum(Sales) / sum(Sales / (1 - Discount)), 2),
    .groups = "drop"
  ) %>%
 arrange(desc(Profit_Margin))

category_sales <- category_sales %>%
  mutate(Note = case_when(
    Profit_Margin == max(Profit_Margin) ~ "Top sub category",
    Profit_Margin == min(Profit_Margin) ~ "Bottom sub category",
    TRUE ~ as.character(NA)
    ))

top_category <- category_sales %>%
  filter(Note == "Top sub category")

bottom_category <- category_sales %>%
  filter(Note == "Bottom sub category")


profit_difference <- top_category$Total_Profit - bottom_category$Total_Profit
margin_dif <- as.numeric(top_category$Profit_Margin) - 
  as.numeric(bottom_category$Profit_Margin)

percentage_tables_sales <- percent(bottom_category$Total_Sales / sum(category_sales$Total_Sales), accuracy = 0.1)


cat("Profit difference between top and bottom categories", profit_difference, "\n")

Profit difference between top and bottom categories 23271.74 

cat("Margin difference between top and bottom categories", percent(margin_dif), "\n")

Margin difference between top and bottom categories 53% 

cat("'Tables' sub category consists of ", percentage_tables_sales, "% of Total Sales\n")

'Tables' sub category consists of  9.0% % of Total Sales

category_sales <- category_sales %>%
  mutate(Profit_Margin = percent(Profit_Margin, , accuracy = 0.01))

year_sales_tables <- superstore %>%
  filter(Sub.Category == "Tables") %>%
  group_by(Order.Year, Order.Month) %>%
  summarise(
    Total.Sales = sum(Sales),
    Total.Profit = sum(Profit),
    Mean.Discount =  1 - round(sum(Sales) / sum(Sales / (1 - Discount)), 2),
    .groups = "drop"
  )


year_sales_tables

category_sales

category_sales <- category_sales %>%
  mutate(Profit_Margin_Numeric = Total_Profit / Total_Sales)

ggplot(category_sales, aes(y = factor(Sub.Category))) +
  geom_bar(aes(x = Total_Sales, fill = "Total_Sales"), stat = "identity", position = "dodge") +
  geom_bar(aes(x = Total_Profit, fill = "Total_Profit"), stat = "identity", position = "dodge") +
  geom_point(aes(x = Profit_Margin_Numeric * max(Total_Sales, Total_Profit), color = "Profit Margin"), size = 2) +
  geom_text(aes(x = Profit_Margin_Numeric * max(Total_Sales, Total_Profit), label = scales::percent(Profit_Margin_Numeric, accuracy = 0.1)) , color = "black", 
            hjust = 1.3, size = 3.5) +
  scale_color_manual(name = NULL, values = c("Profit Margin" = "black", "Discount" = "blue")) +
  
  
    geom_point(aes(x = Mean.Discount * max(Total_Sales, Total_Profit), color = "Discount"), size = 2) +
  geom_text(aes(x = Mean.Discount * max(Total_Sales, Total_Profit), label = scales::percent(Mean.Discount, accuracy = 1.0)), 
            color = "blue", hjust = - 0.5, size = 3.5) +
  
  
  labs(title = "Total Sales, Profit and Profit Margin by Category and Sub.Category",
       y = "Category",
       x = "") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5),
        legend.position = "top",
        axis.title.y = element_blank()) +
  scale_x_continuous(labels = comma) +
  guides(fill = guide_legend(title = NULL), color = guide_legend(title = NULL)) +
  facet_grid(Category ~ ., scales = "free", space = "free") 

# month to month plot for Sales and profit for Tables sub.Category
ggplot(year_sales_tables, aes(x = factor(Order.Month))) + 
  geom_bar(aes(y = Total.Sales, fill = "Total Sales"), stat = "identity", position = position_dodge(width = 0.9), width = 0.8) + 
  geom_bar(aes(y = Total.Profit, fill = "Total Profit"), stat = "identity", position = position_dodge(width = 0.9), width = 0.8) + 
  geom_point(aes(y = Mean.Discount * max(Total.Sales, Total.Profit), color = "Discount"), size = 2) +
  geom_text(aes(y = Mean.Discount * max(Total.Sales, Total.Profit), label = scales::percent(Mean.Discount, accuracy = 1.0)), 
            color = "blue", hjust = -0.5, size = 3, angle = 90) +
  facet_wrap(~Order.Year, nrow = 1) +
  labs(title = "Total Sales, Profit and Discount by Month and Year for 'Tables' sub category", 
       x = "Month", 
       y = "") + 
  theme_minimal() + 
  theme(legend.title = element_blank(), 
        legend.position = "top", 
        axis.title.y = element_blank(), 
        plot.title = element_text(hjust = 0.5)) + 
  scale_y_continuous(labels = comma) +
  scale_color_manual(name = "Discount", values = c("Discount" = "blue")) +
  guides(fill = guide_legend(title = NULL))

Category Analysis

1. Identifying the Category with the Lowest Margins

The category with the lowest margins and profit is “Tables” with a negative profit margin of -8.56%. Analysis of this category by month shows that it has been and remains unprofitable over time, with the maximum loss occurring in the last year of 2017. At the same time, high discounts are regularly applied in this category. A detailed graph for this category shows the dependency of losses on the size of the discount.

2. Comparing Subcategories

• Bottom subcategory - Furniture, Tables

• Top subcategory - Office Supplies, Labels

  • Profit difference between top and bottom categories - 23,271.74
  • Margin difference between top and bottom categories - 53%

3. Conclusions

Quick decision-making to improve the situation can significantly impact the overall Sales and Profit of the entire company, as this category ranks 4th in terms of sales volume and accounts for 9% of the total sales volume. Perhaps, it is necessary to reduce the discount size to such a level that no sale of goods from this category will bring losses in the future. The “Total Sales, Profit and Discount by Month and Year for ‘Tables’ subcategory” graph shows that a slight increase in the discount by 3-5% leads to a significant decrease in profit.

=================================================================

Question 3: How do regional performances compare?

Objective: Compare sales distribution and profitability across regions

Tasks:

• Identify which region has both high sales and high profitability

• Find any regions with negative profits

• Analyze if high sales always correlate with high profits

• Propose regional-specific strategies based on findings

# Sales data by 4 regions
region_sales <- superstore %>%
  group_by(Region) %>%
  summarise(
    Total_Sales = sum(Sales),
    Total_Profit = sum(Profit),
    Profit_Margin = Total_Profit / Total_Sales,
    Mean_Discount = 1 - round(sum(Sales) / sum(Sales / (1 - Discount)), 2),
    .groups = "drop"
  ) %>%
  arrange(desc(Profit_Margin))

# Data for states with negative Profit
negativ_state_profit <- superstore %>%
  group_by(Region, State) %>%
  summarise(
    Total_Sales = sum(Sales),
    Total_Profit = sum(Profit),
    Profit_Margin = Total_Profit / Total_Sales,
    Mean_Discount = 1 - round(sum(Sales) / sum(Sales / (1 - Discount)), 2),
    .groups = "drop"
  ) %>%
  filter(Total_Profit < 0) %>%
  arrange(Profit_Margin)

# Top 10 states by Profit
top10_states_sales <- superstore %>%
  group_by(Region, State) %>%
  summarise(
    Total_Sales = sum(Sales),
    Total_Profit = sum(Profit),
    Profit_Margin = Total_Profit / Total_Sales,
    Mean_Discount = 1 - round(sum(Sales) / sum(Sales / (1 - Discount)), 2),
    .groups = "drop"
  ) %>%
  arrange(desc(Profit_Margin)) %>%
  slice_head(n = 10)



region_sales

negativ_state_profit

top10_states_sales

# Total Sales, Profit and Margin by Regions graph
ggplot(region_sales, aes(y = factor(Region))) +
  geom_bar(aes(x = Total_Sales, fill = "Total_Sales"), stat = "identity", position = "dodge") +
  geom_bar(aes(x = Total_Profit, fill = "Total_Profit"), stat = "identity", position = "dodge") +
  geom_point(aes(x = Profit_Margin * max(Total_Sales, Total_Profit), color = "Profit Margin"), size = 2) +
  geom_text(aes(x = Profit_Margin * max(Total_Sales, Total_Profit), label = scales::percent(Profit_Margin, accuracy = 0.1)),
            color = "black", vjust = 1.8, size = 3) +
  geom_point(aes(x = Mean_Discount * max(Total_Sales, Total_Profit), color = "Discount"), size = 2) +
  geom_text(aes(x = Mean_Discount * max(Total_Sales, Total_Profit), label = scales::percent(Mean_Discount, accuracy = 1.0)), 
            color = "blue", vjust = -0.8, size = 3) +
  scale_color_manual(name = NULL, values = c("Profit Margin" = "black", "Discount" = "blue")) + 
  labs(title = "Total Sales, Profit and Margin by Regions",
       y = "Region",
       x = "") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5),
        strip.text.y = element_blank(),
        legend.position = "top") +
  scale_x_continuous(labels = comma) +
  guides(fill = guide_legend(title = NULL), color = guide_legend(title = NULL)) +
  facet_grid(Region ~ ., scales = "free", space = "free")

# States with negative Profit graph
 negativ_state_profit <- negativ_state_profit %>%
   mutate(State = fct_reorder(State, Total_Profit))

ggplot(negativ_state_profit, aes(y = factor(State))) +
  geom_bar(aes(x = Total_Sales, fill = "Total_Sales"), stat = "identity", position = "dodge") +
  geom_bar(aes(x = Total_Profit, fill = "Total_Profit"), stat = "identity", position = "dodge") +
  facet_grid(~Region) +
  geom_point(aes(x = Profit_Margin * max(Total_Sales, Total_Profit), color = "Profit Margin"), size = 2) +
  geom_text(aes(x = Profit_Margin * max(Total_Sales, Total_Profit), label = scales::percent(Profit_Margin, accuracy = 0.1)) , color = "black", 
            hjust = - 0.2, size = 3.5) +
  scale_color_manual(name = NULL, values = c("Profit Margin" = "black", "Discount" = "blue")) +
  geom_point(aes(x = Mean_Discount * max(Total_Sales, Total_Profit), color = "Discount"), size = 2) +
  geom_text(aes(x = Mean_Discount * max(Total_Sales, Total_Profit), label = scales::percent(Mean_Discount, accuracy = 1.0)), 
            color = "blue", hjust = -0.5, size = 3) +
  labs(title = "Total Sales, Profit and Margin by States with negative Profit",
        y = "", x = "") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5),
        strip.text.y = element_blank(),
        legend.position = "top") +
  scale_x_continuous(labels = comma) +
  guides(fill = guide_legend(title = NULL), color = guide_legend(title = NULL)) +
  facet_grid(State ~ ., scales = "free", space = "free")

# Top 10 best Profit States graph
 top10_states_sales <- top10_states_sales %>%
   mutate(State = fct_reorder(State, Total_Profit))

ggplot(top10_states_sales, aes(y = factor(State))) +
  geom_bar(aes(x = Total_Sales, fill = "Total_Sales"), stat = "identity", position = "dodge") +
  geom_bar(aes(x = Total_Profit, fill = "Total_Profit"), stat = "identity", position = "dodge") +
  facet_grid(~Region) +
  geom_point(aes(x = Profit_Margin * max(Total_Sales, Total_Profit), color = "Profit Margin"), size = 2) +
  geom_text(aes(x = Profit_Margin * max(Total_Sales, Total_Profit), label = scales::percent(Profit_Margin, accuracy = 0.1)) , color = "black", 
            hjust = - 0.2, size = 3.5) +
  scale_color_manual(name = NULL, values = c("Profit Margin" = "black", "Discount" = "blue")) +
  geom_point(aes(x = Mean_Discount * max(Total_Sales, Total_Profit), color = "Discount"), size = 2) +
  geom_text(aes(x = Mean_Discount * max(Total_Sales, Total_Profit), label = scales::percent(Mean_Discount, accuracy = 1.0)), 
            color = "blue", hjust = -0.5, size = 3) +
  labs(title = "Total Sales, Profit and Margin by States with negative Profit",
        y = "", x = "") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5),
        strip.text.y = element_blank(),
        legend.position = "top") +
  scale_x_continuous(labels = comma) +
  guides(fill = guide_legend(title = NULL), color = guide_legend(title = NULL)) +
  facet_grid(State ~ ., scales = "free", space = "free")

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Analysis of Profit, Margin, and Discounts by Region and State

Regional Differences

Regions West and East perform best, with profit and margin above average, largely due to lower average discounts (around 16-19 %). The lowest margin is observed in the Central region, where the average discount is significantly higher (25%).

States with Losses

The main losses are concentrated in states like Ohio (East), Texas (Central), and Pennsylvania (East), where high discount levels (up to 42%) directly result in negative profits.

Impact of Discounts

High discount levels affect profitability more than geographical location. Every region has states with negative profits, always linked to high discount rates around 38%. States with minimal discounts (e.g., in West and East) exhibit consistent growth in margin and profit. However, such states are also present in Central and South regions.

Recommendations for Improvement

1. Reduce discounts in problematic regions and states.
2. Conduct a detailed analysis of specific causes of losses in states with negative profits, such as Ohio, Colorado, Texas, Pennsylvania and Illinois, to identify opportunities for improvement.

===============================================================

Question 4: What does customer segmentation reveal?

Objective: Identify valuable customer groups using RFM analysis

Tasks:

• Calculate percentage of customers in each segment

• Identify which segment generates the most revenue

• Develop retention strategies for “At Risk” customers

• Suggest marketing approaches for “High Spenders”

# Data for each order (aggregated without item-level breakdown)
orders <- superstore %>%
  group_by(Order.ID) %>%
  summarize(
    across(-c(Row.ID, Profit.Margin, Discount, Product.ID, Category, Sub.Category, Product.Name, Sales, Quantity, Profit), first, .names = "{.col}"),
    Order_Sum = sum(Sales),                # order sum
    Order_Quantity = sum(Quantity),          # total quantity of items in the order
    Order_Profit = sum(Profit),              # total order profit
    Order_Mean_Discount = 1 - round(sum(Sales) / sum(Sales / (1 - Discount)), 2),  # average discount
    Order_Margin = round(Order_Profit / Order_Sum, 2)  # profit margin
  )

# Aggregating data by Customer.ID
customer_summary <- orders %>%
  group_by(Customer.ID) %>%
  summarise(
    across(-c(Order.Year, Order.Month, Ship.Date, Order.ID, Order_Sum, Order_Quantity, Order_Profit, Order_Mean_Discount, Order_Margin), first, .names = "{.col}"),
    Total_Orders = n(),                          # total number of orders
    Total_Sales = sum(Order_Sum),                # total sales from all orders
    Total_Profit = sum(Order_Profit),
    Mean_Discount = 1 - round(sum(Order_Sum) / sum(Order_Sum / (1 - Order_Mean_Discount)), 2),
    Ship.Mode = names(which.max(table(Ship.Mode))), # Most frequent Ship.Mode
    .groups = "drop"
  )


last_order_check <- orders %>%
  group_by(Customer.ID) %>%
  summarise(Last_Order_Date = max(as.Date(Order.Date)), .groups = "drop")

# Объединяем с customer_summary
customer_summary <- customer_summary %>%
 # select(-Last_Order_Date) %>%
  left_join(last_order_check, by = "Customer.ID")


# Reference dates for Recency scoring
start_date_2017 = as.Date("2017-07-01")
end_date_2017 = as.Date("2017-12-31")
start_date_2016 = as.Date("2016-07-01")
end_date_2016 = as.Date("2017-06-30")

# Add scores for each criterion
customer_summary <- customer_summary %>%
  mutate(
    # Recency Score (R_Score)
    R_Score = case_when(
      Last_Order_Date >= start_date_2017 & Last_Order_Date <= end_date_2017 ~ 2, # Last order from July to Dec 2017
      Last_Order_Date >= start_date_2016 & Last_Order_Date <= end_date_2016 ~ 1, # Last order from July 2016 to June 2017
      Last_Order_Date < start_date_2016 ~ 0,                                    # Last order before July 2016
      TRUE ~ 0
    ),
    
    # Frequency Score (F_Score)
    F_Score = case_when(
      Total_Orders >= 10 ~ 3,                       # 10 or more orders
      Total_Orders >= 4 & Total_Orders <= 9 ~ 2,    # Between 4 and 9 orders
      Total_Orders >= 2 & Total_Orders <= 3 ~ 1,    # Between 2 and 3 orders
      Total_Orders == 1 ~ 0,                        # Only 1 order
      TRUE ~ 0
    ),
    
    # Monetary Score (M_Score) using ntile() for percentiles
    M_Score = ntile(Total_Sales, 5)  # Divide into 5 groups (1 - lowest, 5 - highest)
  )

# Calculate overall customer score
customer_summary <- customer_summary %>%
  mutate(
    Total_Score = R_Score + F_Score + M_Score  # Sum scores for the total result
  )

customer_summary <- customer_summary %>%
  mutate(
    Score_Color = case_when(
      Total_Score >= 0 & Total_Score <= 4 ~ "red",    # 0-4: red
      Total_Score >= 5 & Total_Score <= 7 ~ "yellow", # 5-7: yellow
      Total_Score >= 8 & Total_Score <= 10 ~ "green"  # 8-10: green
    )
  )

# Summary table for customer counts by Total_Score and Score_Color
score_summary <- customer_summary %>%
  group_by(Total_Score, Score_Color) %>%
  summarise(Count = n(), .groups = "drop")  # Count of customers grouped by Total_Score and Score_Color

# Display results
print(customer_summary)

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# Bar chart to show distribution of Total_Score by Score_Color
ggplot(score_summary, aes(x = factor(Total_Score), y = Count, fill = Score_Color)) +
  geom_bar(stat = "identity", color = "black", width = 0.7) +  # Bar chart with outlined bars
  geom_text(aes(label = Count), vjust = -0.5, size = 4) +      # Numerical labels for the count on top of the bars
  scale_fill_identity(name = "Score Color") +                  # Use colors directly from Score_Color
  labs(
    title = "Distribution of customers based on RFM analysis",
    x = "Total Score",
    y = "Count of Customers"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),  # Centered and bold title
    axis.title = element_text(size = 12),  # Larger axis labels for readability
    axis.text = element_text(size = 10),  # Adjust axis text size
    legend.position = "top"  # Place legend at the top
  ) 

# Scatter plot for customer order analysis with Total_Sales in legend
ggplot(customer_summary, aes(x = Last_Order_Date, y = Total_Orders, size = Total_Sales, color = Score_Color)) +
  geom_point(alpha = 0.7) +  # Points with transparency
  scale_size_continuous(range = c(1, 8), name = "Total Sales (Point Size)") +  # Add name for the size legend
  scale_color_identity(name = "Score Color") +  # Add name for the color legend
  labs(
    title = "Distribution of customers based on RFM analysis",
    x = "Last Order Date",
    y = "Total Number of Orders",
    size = "Total Sales (Point Size)"  # Explain size represents Total Sales
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),  # Centered and bold title
    axis.title = element_text(size = 12),  # Larger axis labels for readability
    axis.text = element_text(size = 10),  # Adjust axis text size
    legend.position = "top"  # Place legend at the top
  )

# Scatter plot for Total Profit vs Mean Discount with Score_Color
ggplot(customer_summary, aes(x = Mean_Discount, y = Total_Profit, color = Score_Color)) +
  geom_point(size = 3, alpha = 0.7) +  # Points with size and transparency
  scale_color_identity() +             # Use colors directly from Score_Color
  labs(
    title = "Profit vs Discount with RFM Coloring",
    x = "Mean Discount",
    y = "Total Profit"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),  # Centered and bold title
    axis.title = element_text(size = 12),  # Larger axis labels for readability
    axis.text = element_text(size = 10),  # Adjust axis text size
    legend.position = "none"  # Remove legend since colors are already explained
  )

# Scatter plot for Total Profit dependency on Total Score
ggplot(customer_summary, aes(x = Total_Score, y = Total_Profit)) +
  geom_point(alpha = 0.7, color = "blue", size = 3) +  # Points with size and transparency
  geom_smooth(method = "lm", se = FALSE, color = "red", linetype = "dashed") + # Linear trend line
  labs(
    title = "Dependency of Total Profit on Total Score",
    x = "Total Score",
    y = "Total Profit"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),  # Centered and bold title
    axis.title = element_text(size = 12),  # Larger axis labels for readability
    axis.text = element_text(size = 10)  # Adjust axis text size
  )

# Scatter plot for Total Profit dependency on Total Sales with Score_Color
ggplot(customer_summary, aes(x = Total_Sales, y = Total_Profit, color = Score_Color)) +
  geom_point(alpha = 0.7, size = 3) +  # Points with transparency and size
  geom_smooth(method = "lm", se = FALSE, color = "black", linetype = "dashed") + # Linear trend line
  scale_color_identity(name = "Score Color") +  # Use colors directly from Score_Color
  labs(
    title = "Dependency of Total Profit on Total Sales by Score Color",
    x = "Total Sales",
    y = "Total Profit"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),  # Centered and bold title
    axis.title = element_text(size = 12),  # Larger axis labels for readability
    axis.text = element_text(size = 10),  # Adjust axis text size
    legend.position = "top"  # Place legend at the top
  )

RFM Analysis Criteria

R (Recency): - 2 points if the last order was within the last 6 months. - 1 point if the last order was between 6 to 18 months ago. - 0 points if the last order was more than 18 months ago.

F (Frequency): - 3 points if the client made 10 or more orders. - 2 points if the client made 4 to 9 orders. - 1 point if the client made 2–3 orders. - 0 points if the client made only 1 order.

M (Monetary Score): - Calculated using the ntile() function to split the data into percentiles, divided into 5 groups (1 = lowest, 5 = highest).

Client Groups Based on RFM Analysis

Based on the RFM analysis, all clients were divided into three groups:

High-value clients (8–10 points, green color in the graphs): - These clients represent the main value for the business. They make more orders, bring high income, and require minimal attention in terms of discounts. - Share of these clients: 37% of the total client base.

Medium-value clients (5–7 points, yellow color in the graphs): - The largest group, making up 48% of the client base. These clients generate part of the profit but require individualized strategies to retain and increase their value.

Low-value clients (0–4 points, red color in the graphs): - Often associated with losses due to high discounts and low order volumes. - Share of these clients: 15% of the total client base.

Summary Based on Graph Analysis

1. Retention of High-Value Clients:
   • A significant share of green points (high-value clients) in the right-side cluster of the second graph (“Distribution of customers based on RFM analysis”) highlights a positive trend in retaining the most valuable clients. These clients continued placing orders until the latest recorded date.
2. Impact of High Discounts:
   • High discounts not only reduce profit but also tend to attract low-value clients (red points). In the third graph (“Profit vs Discount with RFM Coloring”), red points often fall within the zone of discounts exceeding 20%. This suggests that excessive discounts may attract clients who buy only during promotions and lack loyalty or long-term value.
3. Correlation Between Profit, Sales, and Value:
   • In the fourth and fifth graphs, a strong positive correlation is observed between sales and profit, as well as between profit and client category (green, yellow, red). This confirms the importance of focusing efforts on clients contributing the most to total revenue.

Recommendations for Discounts and Profit

• High discounts correlate directly with reduced profitability.
• Optimizing discount policies is critical for increasing profit margins.
• Most loyal clients with high scores (8–10) make frequent orders and tend to require lower average discounts (less than 10%). Retention tools should focus on these high-value clients.

===============================================================

Question 5: How does shipping mode affect profitability?

Objective: Analyze cost-to-serve across shipping modes

Tasks:

• Compare profit margins across shipping modes

• Calculate profit per order for each shipping mode

• Suggest optimal shipping strategy based on findings

total_orders <- nrow(orders)  # Total number of orders in the dataset

ship_mode_summary <- orders %>%
  group_by(Ship.Mode) %>%
  summarise(
    Weighted_Avg_Margin = sum(Order_Margin * Order_Sum, na.rm = TRUE) / sum(Order_Sum, na.rm = TRUE),  # Weighted Margin
    Total_Profit = sum(Order_Profit, na.rm = TRUE),  # Total Profit
    Number_of_Orders = n(),  # Count of orders
    Profit_Per_Order = Total_Profit / Number_of_Orders,  # Profit per order
    Order_Percentage = round((Number_of_Orders / total_orders)*100, 2),  # Percentage of orders for this shipping mode
    .groups = "drop"
  )

max_profit <- max(ship_mode_summary$Total_Profit, na.rm = TRUE)
max_margin <- max(ship_mode_summary$Weighted_Avg_Margin, na.rm = TRUE)
scaling_factor <- max_profit / max_margin


# Print summary table
print(ship_mode_summary)

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# Plot Total Profit and Weighted Avg Margin
ggplot(ship_mode_summary, aes(x = Ship.Mode)) +
  # Bar plot for Total Profit
  geom_bar(aes(y = Total_Profit, fill = Ship.Mode), stat = "identity", color = "black", width = 0.7) +
  # Line connecting Weighted_Avg_Margin points
  geom_line(aes(y = Weighted_Avg_Margin * scaling_factor, group = 1), 
            color = "blue", size = 1) +
  # Points for Weighted_Avg_Margin
  geom_point(aes(y = Weighted_Avg_Margin * scaling_factor), 
             color = "blue", size = 3) +
  # Labels for Weighted_Avg_Margin
  geom_text(aes(y = Weighted_Avg_Margin * scaling_factor, 
                label = percent(round(Weighted_Avg_Margin, 4))), 
            vjust = 1.8, color = "blue", size = 4) +
  # Labels and Titles
  labs(
    title = "Total Profit and Weighted Average Margin by Shipping Mode",
   
    x = "Shipping Mode",
    y = "Total Profit",
    fill = "Shipping Mode"
  ) +
  # Secondary Y-axis for Weighted_Avg_Margin
  scale_y_continuous(
    sec.axis = sec_axis(~ . / scaling_factor, 
                        name = "Weighted Avg Margin (%)")
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),  # Centered and bold title
    axis.title = element_text(size = 12),  # Larger axis labels
    axis.text = element_text(size = 10),  # Adjust axis text size
    legend.position = "none"  # Place legend at the top
  )

ggplot(ship_mode_summary, aes(x = Ship.Mode, y = Profit_Per_Order, fill = Ship.Mode)) +
  geom_bar(stat = "identity", color = "black", width = 0.7) +  # Bar plot for Profit_Per_Order
  geom_text(aes(label = paste0(round(Order_Percentage, 1), "%"), y = Profit_Per_Order), 
            vjust = -0.5, color = "black", size = 3) +  # Add Order_Percentage as labels
  labs(
    title = "Profit Per Order by Shipping Mode",
     subtitle = "Text above each column indicates the percentage of orders out of the total number",
    x = "Shipping Mode",
    y = "Profit Per Order",
    fill = "Shipping Mode"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),  # Centered and bold title
    axis.title = element_text(size = 12),  # Larger axis labels
    axis.text = element_text(size = 10),  # Adjust axis text size
    legend.position = "none"  # Remove legend for a cleaner look
  )

# Plot: Total Profit per Customer by Shipping Mode with Score_Color
ggplot(customer_summary, aes(x = Ship.Mode, y = Total_Profit, color = Score_Color)) +
  geom_jitter(width = 0.2, alpha = 0.7, size = 3) +  # Jitter to avoid overlapping points
  scale_color_identity(name = "Score Color", guide = "legend") +  # Use Score_Color for coloring
  labs(
    title = "Distribution of Customers by Shipping Mode and Total Profit",
    x = "Shipping Mode",
    y = "Total Profit",
    color = "Score Color"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),  # Centered and bold title
    axis.title = element_text(size = 12),  # Larger axis labels for readability
    axis.text = element_text(size = 10),  # Adjust axis text size
    legend.position = "top"  # Place legend at the top
  )

# Step 1: Calculate the number of customers in each Score_Color category for each Ship.Mode
category_distribution <- customer_summary %>%
  group_by(Ship.Mode, Score_Color) %>%
  summarise(Customer_Count = n(), .groups = "drop")

# Step 2: Create the bar plot
ggplot(category_distribution, aes(x = Ship.Mode, y = Customer_Count, fill = Score_Color)) +
  geom_bar(stat = "identity", position = "dodge", color = "black") +  # Bar plot with dodged bars
  scale_fill_identity(name = "Score Color", guide = "legend") +       # Use Score_Color as fill
  labs(
    title = "Customer Distribution by Score Color for Each Shipping Mode",
    x = "Shipping Mode",
    y = "Number of Customers",
    fill = "Score Color"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),  # Centered and bold title
    axis.title = element_text(size = 12),  # Larger axis labels
    axis.text = element_text(size = 10),  # Adjust axis text size
    legend.position = "top"  # Place legend at the top
  )

library(dplyr)
library(ggplot2)

# Step 1: Add Avg_Order_Sum to ship_mode_summary
ship_mode_summary <- orders %>%
  group_by(Ship.Mode) %>%
  summarise(
    Weighted_Avg_Margin = sum(Order_Margin * Order_Sum, na.rm = TRUE) / sum(Order_Sum, na.rm = TRUE),  # Weighted Margin
    Total_Profit = sum(Order_Profit, na.rm = TRUE),  # Total Profit
    Number_of_Orders = n(),  # Count of orders
    Profit_Per_Order = Total_Profit / Number_of_Orders,  # Profit per order
    Avg_Order_Sum = sum(Order_Sum, na.rm = TRUE) / Number_of_Orders,  # Average Order Sum
    .groups = "drop"
  )

Analysis of Shipping Modes

Insights:

1. Most Popular Shipping Mode:
   • Standard Class accounts for almost 60% of all orders.
   • This type of delivery is used by customers across all categories (Score_Color groups).
   • Profit per Order for Standard Class is the lowest compared to other shipping modes.
2. Highest Profit and Margin:
   • First Class delivery has the highest margin (~14%) and highest Profit per Order.
   • Customers opting for First Class likely prioritize speed over cost.
   • These orders tend to have smaller discounts and therefore higher margins.

Recommendations:

• Encourage customers to upgrade to First Class delivery where possible.
• This can serve as an intangible loyalty incentive offered by the superstore, potentially increasing customer satisfaction.
• Promote First Class delivery as an alternative to large discount-based incentives that could negatively impact profit margins.

===============================================================

Extra Challenge

• Identify 3 actionable business recommendations.

  • Optimize Discount Strategy:

    • Reduce excessive discounts (above 20%) in unprofitable categories (like “Tables”) and problematic regions/states (e.g., Ohio, Texas, Pennsylvania).

    • Focus on offering discounts only to high-value clients or in high-margin categories to avoid unnecessary profit losses.

  • Focus on High-Value Clients:

    • Retain and nurture high-value clients (RFM scores 8–10) by offering personalized loyalty programs and benefits.

    • These clients bring the highest profitability and require minimal discounts (typically less than 10%).

  • Leverage High-Margin Shipping Options:

    • Promote First Class shipping, which has the highest profit margins and order profitability, as a premium service.

    • Use this as a strategy to reduce the dependency on discount-based incentives.

• Propose 2 new questions for deeper analysis.

• How do discounts influence customer retention and lifetime value across different RFM segments?

  • By analyzing the relationship between discount levels and customer behavior within each RFM group (high, medium, and low value), you can identify optimal discount strategies to retain high-value clients without sacrificing profitability.

• What product categories contribute the most to repeat purchases, and how does their profitability compare across different regions?

  • Understanding the regional performance of top categories and their role in driving repeat business can guide targeted marketing efforts and inventory optimization for specific locations.

• Estimate potential financial impact of your recommendations.

negative_profit_2017 <- superstore %>%
  filter(Order.Year == "2017" & Profit < 0) %>%  
  summarise(Total_Negative_Profit = sum(Profit, na.rm = TRUE)) #
negative_profit_2017

If the company simply refuse to fulfill all orders that result in losses, it would be possible to avoid losses amounting to -53,836 next year.

Discussion Points:

How do sales trends correlate with marketing initiatives?

Are there regional preferences for product categories?

What operational changes could improve low-margin categories?

How might customer segmentation affect inventory management?